1 GEODETICKÝ a KARTOGRAFICKÝ obzor obzor Český úřad zeměměřický a katastrální Úrad geodézie, kartografie a katastra Slovenskej republiky 12/2014 Praha, prosinec 2014 Roč. 60 (102) o Číslo 12 o str
2 POZVÁNKA NA KONFERENCI VYSOKÉ UČENÍ TECHNICKÉ V BRNĚ FAKULTA STAVEBNÍ 17. odborná konference doktorského studia Fakulta stavební Vysoké učení technické v Brně PROGRAM KONFERENCE ČTVRTEK 29. LEDNA 2015 Prezence účastníků konference Slavnostní zahájení konference v aule Fakulty stavební Jednání v sekcích Oficiální zakončení konference v aule Fakulty stavební Společenský večer ADRESA Vysoké učení technické v Brně Fakulta stavební Veveří 331/ Brno TERMÍNY Uzávěrka přihlášek Uzávěrka odevzdání příspěvků Konference JUNIORSTAV 2015 KONTAKT web: juniorstav2015.fce.vutbr.cz facebook.com/juniorstav
3 ročník 60/102, 2014, číslo Obsah MSc. Paweł Hordyniec Modelling of Zenith Tropospheric Delays and Integrated Water Vapour Values Ing. Tomáš Mikita, Ph.D. Hodnocení přesnosti digitálního modelu povrchu 1. generace v lesních porostech a možnosti využití dat v lesnické praxi Z ČINNOSTI ORÁNOV A ORGANIZÁCIÍ SPOLEČENSKO-ODBORNÁ ČINNOST ZPRÁVY ZE ŠKOL Z GEODETICKÉHO A KARTOGRAFICKÉHO KALENDÁŘE Z MEDZINÁRODNÝCH STYKOV Modelling of Zenith Tropospheric Delays and Integrated Water Vapour Values MSc. Paweł Hordyniec, Institute of Geodesy and Geoinformatics, Wroclaw University of Environmenta and Life Sciences Summary Description of the most common sources of meteorological parameters: in-situ measurements, empirical atmosphere models such as UNB3m (University of New Brunswick model), Global Pressure and Temperature (GPT and GPT2), standard atmosphere model of Berg as well as Numerical Weather Prediction (NWP) Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS). These models are used to determine tropospheric delay components in zenith direction and total amount of water vapour along GNSS signal path. Based on analyses performed over the territory of Poland at ASG-EUPOS sites of Ground Base Augmentation System (GBAS), COAMPS is shown to deliver very accurate parameters in terms of bias and standard deviation for ZHD (-5,95 mm, σ=3,97 mm) and IWV (0,51 mm, σ=0,57 mm), with meteorological observation available in one-hour interval. Modelování troposférického zpoždění v zenitovém směru a hodnoty integrovaných vodních par Abstrakt Analýza nejčastějších zdrojů meteorologických parametrů, mezi které patří měření in-situ, empirické atmosférické modely jako je UNB3m (University of New Brunswick model), Global Pressure and Temperature (GPT a GPT2), standardní atmosférický model Berg či numerické předpovědní modely počasí (NWP). Výstupy těchto modelů či měření in-situ jsou v oblasti meteorologie GNSS využívány při určování složek troposférického zpoždění a celkového množství vodních par podél cesty signálu GNSS. Na základě provedených analýz nad územím Polska pro referenční stanice GNSS zařazené do sítě Ground Base Augmentation System (GBAS) ze získaných hodnot systematických chyb a směrodatných odchylek vyplývá, že model COAMPS (Coupled- Ocean/Atmosphere Mesoscale Prediction Systém) je schopen poskytovat velmi přesné odhady zenitového hydrostatického zpoždění ZHD (-5,95 mm, σ = 3,97 mm) a množství vodních par IWV (0,51 mm, σ = 0,57 mm) pro meteorologické observace dostupné v jednohodinovém intervalu. Keywords: atmosphere model, GNSS meteorology, IWV, NWP, troposphere delay 1. Introduction GNSS (Global Navigation Satellite System) is one of techniques that allow continuous remote monitoring of atmosphere by analyzing the satellite signal characteristics. Part of distortions comes from the troposphere the closest layer to the Earth s surface. Its impact can be estimated if there are required meteorological parameters known at the observation site. These parameters are air pressure, air temperature and air humidity. To provide the meteorological parameters, various methods have been developed that differ in spatial and temporal resolution, accuracy, input parameters etc. The potential of ground-based observations to derive path delays and IWV has been presented in many studies of global and regional analyses , , . The benefits of numerical weather prediction models have been demonstrated as they can produce high quality parameters for tropospheric delays modeling. The influence of troposphere on GNSS signal propagation results in a delay of the signal which is usually determined for the zenith direction between a satellite and a receiver. For this reason it is called Zenith Total Delay (ZTD). The total delay consists of two independent parts hydrostatic and wet. Thus, ZTD can be expressed as a sum of Zenith Hydrostatic Delay (ZHD) and Zenith Wet Delay (ZWD) ZTD = ZHD + ZWD, (1) where both, ZHD and ZWD are typically expressed in meters.
4 310 ročník 60/102, 2014, číslo 12 Hordyniec, P.: Modelling of Zenith Tropospheric... The usual amount of ZTD is about 2, 3 m at the sea level and most of it (about 90 %) stems from the hydrostatic part. Because the delay is a function of meteorological parameters, modeling the state of the atmosphere is the way how to reduce the troposphere impact. On the other hand, because the ZTD is related to the atmospheric conditions, it can be used as an input and assimilated into numerical weather forecast models to improve their outputs , . The hydrostatic part of ZTD the ZHD is a pressure-dependent parameter that can be modeled with high accuracy, for example by Saastamoinen model , using equation 0, p ZHD =, (2) 1 0,00266 cos2φ 0, h where p is surface pressure in hpa, φ is station latitude, h is ellipsoidal height in meters. The Saastamoinen equation for the wet part reads as follows ( 1255 ZWD = 0, ,05 e, (3) T where T is surface temperature in Kelvin, e is water vapour partial pressure in hpa. In contrary to ZHD, the wet component ZWD is very difficult to model accurately using only surface measurements. Rapid changes in water vapour partial pressure, both in time and space, have an effect on high variability in ZWD. On the other hand, the amount of ZWD in the total delay is rather small. The wet component ZWD is highly related to water amount in the atmosphere. Thus, it is possible to convert ZWD into Integrated Water Vapour (IWV), which is com- ( monly used parameter in meteorology for description of the total amount of water in a vertical column above observation site. According to (4), IWV can be calculated as ZWD IWV =, (4) k 10-6 k R w ( where IWV is expressed in kg/m 2, ZWD is wet delay in meters, k 2 and k 3 are empirical coefficients of refractivity, T m is weighted mean temperature above the observation site in Kelvin, R w is gas constant for wet air (461,525 J K -1 kg -1 ). The empirical constants were defined in a number of investigations , , . The coefficients depend on a refractivity index that can be expressed as variable of partial pressures (dry and wet) and temperature. Several solutions are presented in Tab. 1. In this study, best available coefficients by Rüeger  were used. The supplementary k 2 coefficient is received from other coefficients and constants M k 2 = k 2 k w 1, (5) M d where M w and M d are molar masses for wet and dry air that equal to 18,0152 and 28,9644 g mol -1, respectively. Instead of calculating separately each component of total delay from a priori Saastamoinen model, using (2) and (3), the total delay ZTD can be determined directly from GNSS data processing. By subtracting the hydrostatic part (ZHD) from total delay (ZTD), the wet part (ZWD) is given. This approach to obtain ZWD is generally widely used. Fig. 1 summarizes these two available ways for ZWD determination, including ZWD to IWV conversion. T m ( Tab. 1 Empirical coefficients Reference k 1 [K hpa -1 ] k 2 [K hpa -1 ] k [K 2 hpa -1 ] Smith and Weintraub  Bevis et al.  Rueger best available  77,607 77,600 77,695 71,6 70,4 72,0 3,747 3,739 3,754 Fig. 1 Methodology scheme
5 Hordyniec, P.: Modelling of Zenith Tropospheric... ročník 60/102, 2014, číslo As Fig. 1 shows, corresponding meteorological parameters are required to determine the delay components and IWV. When using a priori Saastamoinen model for ZHD determination, only surface pressure measurements are needed. ZWD can be then derived using ZTD from GNSS processing. Otherwise, a priori ZWD has to be calculated from partial water vapour pressure e and temperature T. In the final conversion, IWV is in both methods calculated using weighted mean temperature T m. This study aims to assess the uncertainties in various meteorological parameters sources and their impact on ZTD components: ZHD, ZWD and derivable IWV. 2. Tested Sources of Meteorological Parameters Values of the tropospheric zenith delays ZHD, ZWD and IWV were calculated for the period of 62 days from De- cember 1 st 2012 to January 31 th In this paper, three main sources of meteorological data were used: in-situ measurements; Numerical Weather Prediction (NWP) model Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS)  and empirical atmosphere models including Global Pressure and Temperature GPT  and GPT2  and University of New Brunswick UNB3 modified model  and Berg model . The first group of data in Fig. 2 is represented by in-situ observations, which are air pressure, air temperature, and relative air humidity measurements. The parameters were collected from synoptic stations: SYNOP belonging to Polish Institute of Meteorology and Water Management (49 stations), METAR (METeorological Aerodrome Report) stations at airports (9 stations) and six GNSS stations equipped with meteorological sensors (marked as GNSS + MET). Fig. 3 shows locations of ground stations together with reference GNSS sites of ASG-EUPOS network. m Fig. 2 Meteorological sources Fig. 3 Locations of the stations used in the study
6 312 ročník 60/102, 2014, číslo 12 Hordyniec, P.: Modelling of Zenith Tropospheric... In-situ data had one-hour temporal resolution. Because all presented results were carried out for ASG-EUPOS sites (GNSS and GNSS + MET in Fig. 1), the meteorological parameters had to be interpolated from their origin to desired location, except sites equipped with meteorological instruments (GNSS + MET). The average distance for both meteorological and GNSS stations is 70 km. Thus, the nearest-neighbour (NN) method was utilized and a correction applied as described in subsection 2.1. Data from numerical weather prediction model COAMPS were provided by The Applied Geomatics Centre (CGS) of Military University of Technology in Warsaw. Parameters were gathered in staggered Arakawa C-grid available in various map projections. Model has 30 vertical levels in a terrain-following sigma-z coordinate to approximately 34 km of height. The mesoscale and non-hydrostatic model uses boundary conditions from Navy Operational Global Atmospheric Prediction System (NOGAPS). The most important parameters of COAMPS consist of Exner functions, wind components (u, v, w), mixing ratios or aerosols concentration. Pressure, temperature and relative humidity, used in this study, are provided in 1-hour interval based on forecast launched at 00 and 12 UTC. All parameters are determined at station coordinates throughout interpolation from grid nodes of 13 km horizontal resolution. Interpolation for temperature and relative humidity was based on weighted average using four grid points  and nearest-neighbour for pressure applying Karabatić  vertical correction (6). The empirical atmosphere models, often called blind, provide needed parameters at given location, demanding only the coordinates (φ, λ, h) and day of the year (DOY). The Berg model requires only station height, but outcomes are constant in time. For UNB3m and GPT, the resolution is annual, with annual and semiannual amplitudes of parameters for GPT2. According to the paper by Bosy et al. , ZTD estimates, available for each GNSS site, were processed in near-real time (NRT) in Bernese software 5.0  in 1-hour window. Observations were used together with Ultra-Rapid CODE products in double-difference (DD) strategy. 21 European Permanent Network (EPN) sites created the base network. The reference network was combined with GNSS sites in baseline design of SHORTEST solution . ZTD estimates were calculated using a priori Saastamoinen model with Niell mapping function  and L5/L3 (wide-lane/narrow-lane) linear combination of signals , . 2.1 Height Correction of Pressure and Temperature The in-situ measurements were the subject of modification as they are interpolated horizontally and vertically. The height correction was applied for parameters from synoptic i.e. SYNOP and METAR sites moved to GNSS sites through nearest-neighbor method. The GNSS + MET ones do not require such adjustment as meteorological sensors are placed close to GNSS reference antennas. The difference between GNSS height (h GNSS ) and its nearest synoptic site height (h NN ) was corrected in terms of pressure and temperature values. To be consistent with COAMPS model, the pressure correction was based on Karabatić  formula ( T P GNSS = P NN NN d T (h NN h GNSS ) R D T, T NN ( g M d (6) where P NN and T NN are in-situ pressure [hpa] and temperature [K], respectively at height of nearest synoptic site (h NN ), d T = 0,0065 K is temperature lapse rate, M d = 28,9644 g mol -1 is molar mass of dry air, R = 8,31432 N m (mol K) -1 is universal gas constant. The gravity acceleration g is calculated using station latitude φ according to the equation  ( h g = 9, NN + h GNSS 2 1 0, cos(2φ) + 5, cos 2 (2φ). ( Temperature reduction uses constant temperature gradient d T according to the height difference as shown in (8) r T = -d T (h GNSS h NN ). Application of selected methods for height correction resulted in 1,1 hpa and 0,9 K of mean bias, respectively for pressure and temperature, with respect to NWP model parameters. While the standard temperature lapse rate equals to 0,65 K for 100 m of height difference, the pressure gradient is approximately 12 hpa. The value of 1 hpa pressure uncertainty can be also expressed as a ZHD component equivalent to approx. 2,3 mm of error, which is expected uncertainty of in-situ data used in this study. 2.2 Water Vapour Partial Pressure and Mean Temperature The water vapour partial pressure e is required to compute ZWD with Saastamoinen model. Unfortunately, such parameter is unspecified in the majority of sources. Thus, the equation of Clausius-Clapeyron ,  was used to compute e based on the relative humidity data, surface temperature and constants ( RH L 1 1 e = e 0 exp (, (9) 100 R e T 0 T where T 0 = 273,15 K is the temperature of freezing, e 0 = 6,11 hpa is the water vapour partial pressure for T 0, L = 2, J kg -1 represents latent heat of vaporization, R e = = 461 J K -1 kg -1 is specific gas constant for water vapour. To calculate the mean temperature, which is needed for ZWD to IWV conversion (4), the linear function of surface temperature T was utilized. The virtual mean temperature is given in Kelvin by the equation of Bevis et al.  3. Case Study T m = 70,2 + 0,72 T. ( ( ( ( (7) (8) (10) In the following section, ZTD components calculated from the in-situ data are considered as reference for comparative analyses. Bias and standard deviation were used for the comparisons. The statistical values were calculated as means for the whole dataset, with a separation on chosen data sources: in-situ, NWP, and empirical models. ZTD estimates were known for 121 GNSS stations of ASG-EUPOS network in an hour interval, including neighboring abroad sites. Thus, hourly meteo-observations were needed to provide homogeneous ZHD values.
7 Hordyniec, P.: Modelling of Zenith Tropospheric... ročník 60/102, 2014, číslo Fig. 4 shows the accuracy of models for ZHD component, referenced to GNSS stations labeled with numbers. Some residuals can be seen for NWP. These refer to abroad stations that are usually located at high altitudes. Numerical weather prediction model may not provide sufficient accuracy for pressure in such situation. If the stand-off stations were discarded, the standard deviation would not exceed 3 mm. Otherwise, it can be as high as 17 mm, with over 60 mm bias. The most erroneous empirical model is the Berg model, with 10 mm mean bias and 17 mm standard deviation (Tab. 2). As the others are very similar in terms of standard deviation, empirical models are shifted with respect to GPT2 by 2 mm, 4 mm and 6 mm, respectively for GPT, UNB3m and Berg, to show model differences at each station. The most unbiased is UNB3m (2,63 mm). Fig. 5 for station WROC (Wroclaw, PL) provides a crucial output to understand, how empirical models work. On the example of a priori plots (left) for the wet component, values from GPT2 and UNB3m are nearly stable in time for short periods (62 days of analysis), and as was written before, Berg is constant. Regardless the small biases from the ZHD estimation, caused mainly by the pressure, the models are only averaging the parameters in annual scale. The plots for ZWD calculated from ZTD-ZHD (right) are very close to NWP characteristic, except almost constant ZHDs for atmosphere models. Thus, the differences of ZWDs (dzwd) arise from ZHD uncertainties. This proves Fig. 5 with reference ZWD marked with cyan. The ZWD standard deviations are actually the same as for ZHD, while biases are the exact opposites (Fig. 6). Thus, estimation of ZWD by subtracting the modeled ZHD and measured ZTD causes only minimal errors (see Tab. 2). Therefore, the ZWD from ZTD-ZHD for in-situ data is taken for further comparisons as reference (best case scenario). However, some differences between ZHD and ZWD statistics are seen as there are stations with no ZTD estimates available. They usually refer to abroad sites and result in additional gaps in data as showed in Fig. 6. If GNSS ZTD measurements are unavailable, ZWD has to be calculated from a priori model (Saastamoinen) using meteorological parameters. Due to the fact that GPT allows only computation of pressure and temperature, ZWD value cannot be computed using this model. In Fig. 7 the best agreement in biases can be seen for GPT2 empirical model (red line) and NWP (blue line). UNB3m results are mostly underestimated the overall positive bias is over 19 mm (Tab. 2). Mean standard deviations for ZWD a priori are ranging from 8,95 mm for NWP up to almost 19 mm for UNB3m. However, intentional shifts of 2 mm and 4 mm are applied for UNB3m and Berg model, respectively, as differences at stations are difficult to distinguish. Comparing the results of ZWD a priori from Saastamoinen model, the empirical models differ significantly in systematic error from -6 mm for GPT2 to -33 mm for the Berg model. To summarize the performed ZWD analysis, Fig. 8 shows the difference in spatial distribution of ZWD component. Fig. 4 ZHD mean bias and standard deviation w.r.t ZHD in-situ Tab. 2 Mean bias (standard deviation) in mm w.r.t. in-situ data (Zenith Delays) Component Solution NWP GPT2 GPT UNB3m BERG ZHD ZWD ZWD Saastamoinen ZTD-ZHD Saastamoinen -5,95 (3,67) 3,45 (3,65) -3,34 (8,95) -7,15 (16,82) 7,92 (16,03) -6,11 (18,54) -6,62 (16,87) 7,31 (16,09) - 2,63 (16,82) 3,73 (16,09) 19,90 (18,65) 7,23 (16,84) -6,24 (16,20) -32,53 (18,42)
8 314 ročník 60/102, 2014, číslo 12 Hordyniec, P.: Modelling of Zenith Tropospheric... Fig. 5 ZWDs from a priori Saastamoinen model (left) and ZWD from ZTD estimates (right) and their differences w.r.t. ZWD in-situ at station WROC (118) Fig. 6 ZWD mean bias and standard deviation w.r.t ZWD in-situ (from ZTD-ZHD) Maps represent NWP mean ZWDs for 62 days and two used solutions: ZWD from ZTD estimates and Saastamoinen ZHD (left) and ZWD from Saastamoinen model (right). As visible, ZWD from ZTD-ZHD is spatially irregular. There is almost no correlation with topography. On one hand, ZWD is at very low level over some mountainous areas in the south of the country. On the other hand, similar values of this component occur at the location of Masurian Lake District (sites LAMA, OLST), which is a lowland in north-east. However, the effect is primarily caused by poorly modeled ZHDs at these sites that produce too low or no wet delays. For ZWD a priori, spatial distribution is smooth and gradually grows from the east to the west of the country. The conversion from ZWD to IWV was performed for both, ZWD a priori and ZWD = ZTD ZHD. IWV is expressed in kg/m 2 that is equal to linear value of millimeter. Outcomes are presented for few chosen stations with different ellipsoidal heights: DRWP (Drawsko Pomorskie, PL) h = 171,1 m, DZIA (Działdowo, PL) h = 206,6 m, ELBL (Elbląg, PL) h = 52,7 m, GANP (Poprad Gánovce, SK) h = 746,0 m. There is an obvious error for NWP COAMPS on station GANP, which is located in Slovakia at high altitude (biased by 6 kg/m 2 ). This case confirms mismodeling already shown for pressure. Station labelled by No. 25 (Fig. 4), which is
9 Hordyniec, P.: Modelling of Zenith Tropospheric... ročník 60/102, 2014, číslo Fig. 7 Mean bias and standard deviation of ZWD a priori from Saastamoinen model w.r.t ZWD in-situ (from ZTD-ZHD) Fig. 8 Mean ZWD from ZTD-ZHD (left) and ZWD a priori (right) for NWP GANP, has mean bias about -40 mm, what indicates overestimation of ZHD with respect to reference in-situ data. The error was transferred to ZWD causing too low IWV value (right plot in Fig. 9). This is because the calculated ZHD using COAMPS pressure was higher than the total delay. Thus, ZWD from subtraction cannot be negative. Results for IWV (Fig. 9) using a priori ZWD in the conversion are more than twice biased, except the Berg model. There are almost no differences between models in terms of standard deviation. Uncertainties for the second conversion method can be grouped into three diverse sets of models: least accurate with the Berg model, moderate (GPT, GPT2, UNB3m), and distinctly different NWP (with the exception of mismodeled site GANP). Based on the Tab. 3, the overall accuracy of COAMPS is -0,52 (σ = 3,13) kg/m 2 for a priori and 0,51 (σ = 0,57) kg/m 2 for ZWD from ZTD ZHD (Tab. 3). All GPT models have very similar errors. Smaller bias and standard deviation holds UNB3m. Same as ZWD a priori, the results for IWV a priori are also less accurate due to connection with poorly modelled ZWD. 4. Conclusion The research proves that numerical weather prediction model COAMPS is the best tested potential source of meteorological parameters when direct observations are unavailable. It provides outputs with high temporal (1 hour) and spatial (13 km grid) resolution. Calculated values of ZHD are biased by -5,95 mm with standard deviation of 3,67 mm compared to those gained from in-situ measurements. On the other hand, NWP is inaccurate for sites at higher altitudes that probably stems from pressure reduction and interpolation method applied in the model. In contrary to NWP, empirical models are easily accessible for pa-
10 316 ročník 60/102, 2014, číslo 12 Hordyniec, P.: Modelling of Zenith Tropospheric... Fig. 9 Mean bias and standard deviation [kg/m 2 ] for IWV from ZWD a priori (left) and IWV from ZWD using ZTD estimates (right) w.r.t. IWVs in-situ Tab. 3 IWV mean bias (standard deviation) in kg/m 2 w.r.t. in-situ data for two ZWD solutions Component Solution NWP GPT2 GPT UNB3m BERG IWV IWV ZWD = ZTD-ZHD ZWD a priori 0,51 (0,57) -0,52 (3,13) 1,17 (2,44) -0,95 (3,96) 1,05 (2,46) - 0,94 (2,40) 3,24 (3,97) -1,55 (2,55) 2,67 (3,98) rameters calculation as only site coordinates are needed. However, their temporal resolution is reduced to one value of parameter per day and the ZHD standard deviation was in this study close to 17 mm. A priori solution used in analysis may cause significant biases for Berg and UNB3m empirical models. However, GPT2 statistics are similar to ZWD calculated from ZTD estimates, but negatively biased. If ZWD is modeled from meteorological parameters derived from NWP, it results in -3,34 mm bias and 8,95 mm standard deviation. When we use ZTD estimates and ZHD Saastamoinen model, the obtained uncertainty of ZWD for NWP gives 3,45 mm bias and 3,65 mm standard deviation. In the final conversion, IWV parameter is almost unbiased with 0,57 kg/m 2 of standard deviation. The results of IWV uncertainties are highly comparable to the other studies. The agreement with respect to ECMWF analysis  is better than 0,25 mm in bias and 2,50 mm in standard deviation for Europe. The needs presented in the paper by Karabatić et al.  are fulfilled for ZWD from NWP model (5 mm of error) and for IWV (under 1 mm error), when using GNSS ZTD estimates. Thus, the COAMPS model meets the COST 716 requirements for numerical weather prediction being a model with high resolution and IWV accuracy between 1 and 5 kg/m 2 . Acknowledgement: This work has been realized in the frame of COST Action ES1206 Advanced Global Navigation Satellite Systems tropospheric products for monitoring severe weather events and climate (GNSS4SWEC) (gnss4swec.knmi.nl). I also thank the Head Office of Geodesy and Cartography for providing the GNSS data from ASG-EUPOS (www.asgeupos.pl) network, the Centre of Applied Geomatics of Military University of Technology in Warsaw, OGIMET service (www.ogimet.com) and National Oceanic and Atmospheric Administration (www.noaa.gov) for providing the NWP model and meteorological data and the Wroclaw Centre of Networking and Supercomputing (www.wcss.wroc.pl): computational grant using Matlab Software License No: LITERATURE:       DE HAAN, S.-BARLAG, S.: Amending Numerical Weather Prediction forecasts using GPS Integrated Water Vapour: a case study. HEISE, S. et al.: Integrated water vapor from IGS ground-based GPS observations: initial results from a global 5-min data set. In: Annales Geophysicae. Copernicus GmbH, 2009, p KARABATIĆ, A.-WEBER, R.-HAIDEN, T.: Near real-time estimation of tropospheric water vapour content from ground based GNSS data and its potential contribution to weather now-casting in Austria. Advances in Space Research, 2011, 47.10: POLI, P. et al.: Forecast impact studies of zenith total delay data from European near real time GPS stations in Météo France 4DVAR. Journal of Geophysical Research: Atmospheres ( ), 2007, 112.D6. DE HAAN, S.: Assimilation of GNSS ZTD and radar radial velocity for the benefit of very short range regional weather forecasts. Quarterly Journal of the Royal Meteorological Society, 2013, : SAASTAMOINEN, J.: Contributions to the theory of atmospheric refraction. Bulletin Geodesique, 1972, 105.1:
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Michal Kačmařík, Ph.D., VŠB TU Ostrava Hodnocení přesnosti digitálního modelu povrchu 1. generace v lesních porostech a možnosti využití dat v lesnické praxi Ing. Tomáš Mikita, Ph.D., Lesnická a dřevařská fakulta, Mendelova univerzita v Brně Abstrakt Od roku 2009 je v rámci společného projektu Českého úřadu zeměměřického a katastrálního, Ministerstva obrany a Ministerstva zemědělství České republiky (ČR) vytvářen nový výškopisný model ČR. Součástí projektu je rovněž vytvoření digitálního modelu povrchu 1. generace (DMP 1G), který může nalézt řadu uplatnění i v lesnictví. Technologie tvorby nového výškopisného modelu je založena na zpracování dat leteckého laserového skenování do podoby souvislého digitálního modelu reliéfu a povrchu ve formě výškových bodů. Cílem je zhodnotit přesnost dat DMP 1G v lesních porostech a vybrat nejvhodnější interpolační metodu pro tvorbu rastrových digitálních modelů. Výšková přesnost dat je zde hodnocena jednak na zaměřené výzkumné ploše u jednotlivých stromů, jednak na základě porovnání středních výšek lesních porostů vypočtených z dat DMP 1G s daty lesního hospodářského plánu. Navíc jsou prezentovány další možnosti využití dat, např. pro analýzu zápoje lesních porostů. Accuracy Evaluation of Digital Surface Model of the 1 st Generation in Forest Environment and its Utilization in Forestry Practice Summary Since 2009 a new elevation model of the Czech Republic has been created as a part of the project of the Czech Office for Surveying, Mapping and Cadastre, Ministry of Defence and Ministry of Agriculture. The project includes creation of a digital surface model of the first generation (DSM 1G DMP 1G), which can be used for a number of applications also in the forestry. The technology of its creation is based on the processing of airborne LiDAR data to the form of continuous digital elevation model represented by height points. The aim of this work is not only accuracy evaluation of these data especially in conditions of forest stands but also choosing of the most suitable interpolation technique for creation of raster digital models. Within this work the height accuracy of data is evaluated both for individual trees measured by geodetic surveying on the test site and by comparing the heights of forest stands calculated from DSM 1G and heights taken from the forest management plan. Moreover further opportunities of data usage e.g. analysis of canopy density are presented. Keywords: LiDAR, elevation model of Czech Republic, GIS interpolation