Integrated Water Vapour

The spatial and temporal variations of atmospheric precipitable water (PW) content anomalies were analysed over Europe from 1973 to 2003 using daily data (0000 and 1200 UTC) from National Center of Environmental Prediction and National Center of Atmospheric Research Reanalysis project (NCEP‐1) and in situ radiosonde data. Mann–Kendall trend tests were applied to long‐term PW time series. Technology changes influence PW radiosonde trends, although these are in agreement with NCEP‐1 trends. Over the south of the Iberian Peninsula, trends are negative and statistically significant (<− 0.04 mm year−1; p < 0.05) and positive over the Central European Mountains (The Alpes) and the North Atlantic Ocean (>0.04 mm year−1; p < 0.05). Seasonal trends revealed negative and significant trends over the Iberian Peninsula for all seasons (<− 0.03 mm year−1; p < 0.05). Copyright © 2010 Royal Meteorological Society

Atmospheric Water vapor is an important greenhouse gas and contributes greatly in maintaining the Earth's energy balance. This critical meteorological parameter is not being sensed by any of the 22 synoptic weather stations in Ghana. This study presents a highly precise tool for water vapor sensing based on the concept Global Navigation Satellite Systems (GNSS) meteorology and tests the computed results against global reanalysis data. Conventional approaches used to sense the atmospheric water vapor or Precipitable Water (PW) such as radiosondes, hygrometers, microwave radiometers or sun photometers are expensive and have coverage and temporal limitations. Whereas This study employed precise point positioning (PPP) techniques to quantify the extend of delays on the signal due to the troposphere and stratosphere where atmospheric water vapor resides. Stringent processing criteria were set using an elevation cut-off of 5 degrees, precise orbital and clock products were used as well as nominal tropospheric corrections and mapping functions implemented. The delays which are originally slanted are mapped unto the zenith direction and integrated with surface meteorological parameters to retrieve PW or IWV. The gLAB software, Canadian Spatial Reference System (CSRS) and Automatic Precise Positioning Service (APPS) online PPP services were the approaches used to compute ZTD. PW values obtained were compared with Japanese Metro Agency Reanalysis (JRA), European Centre for Medium-Range Weather Forecasts Reanalysis (ERA-interim) and National Center for Environmental Prediction (NCEP) global reanalysis data. Correlation analysis were run on the logged station data using the three approaches and global reanalysis data. The obtained results show stronger correlation between the retrieved PW values and those provided by the ERA-interim. Finally, the study results indicate that with a more densified network of GNSS base stations the retrieved PW or IWV will greatly improve numerical weather predictions in Ghana.

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Surface‐based GPS measurements of zenith path delay (ZPD) can be used to derive vertically integrated water vapor (IWV) of the atmosphere. ZPD data are collected in a global network presently consisting of 160 stations as part of the International GPS Service. In the present study, ZPD data from this network are converted into IWV using observed surface pressure and mean atmospheric water vapor column temperature obtained from the European Centre for Medium‐Range Weather Forecasts' (ECMWF) operational analyses (OA). For the 4 months of January/July 2000/2001, the GPS‐derived IWV values are compared to the IWV from the ECMWF OA, with a special focus on the monthly averaged difference (bias) and the standard deviation of daily differences. This comparison shows that the GPS‐derived IWV values are well suited for the validation of OA of IWV. For most GPS stations, the IWV data agree quite well with the analyzed data indicating that they are both correct at these locations. Larger d...

Hygrometric ratio measurements were simultaneously taken on six autumn clear-sky days of 1981 and 1982 by employing four Volz sun-photometers and the FISBAT sun-photometer at five stations located at different altitudes along the western slope of the Leo Valley, in the Apennines (Italy). Due to the solar heating of ground, intense upslope breezes forming during the early morning caused the vertical transport of more humid air from the bottom of the valley toward the ridge of the mountain chain. Precise calibration curves of the hygrometric ratio were defined on the basis of criteria suggested by the atmospheric infrared hygrometry technique and using the calibration constants found through an accurate intercomparison procedure. Examining the sun-photometric measurements by means of these calibration curves, precipitable water was determined at all stations, with the frequency of one measurement every 15 minutes from the early morning to one hour after noon. Daily homogeneous time-patterns of precipitable water were defined at the various stations, showing that this quantity varies appreciably during the morning at all stations, sometimes presenting daily increases of more than 40% at the lower stations. Average values of absolute humidity were then determined within the four atmospheric layers defined by the station altitudes, finding that the convective transport of humid air along the valley slopes can produce important variations within the atmospheric layer below the 1.6 km height.

Rain is one of the fundamental processes of the hydrologic cycle as it can be the source of wealth or natural hazards. This experiment focuses in the relationship between rain occurrence and atmospheric pressure (Patm) and atmospheric water vapor content (PW), GPS estimated. The available nine years time series of each variable were analyzed. It allowed to state the existence of three rain patterns and monthly differences in the Patm-PW combinations. In spite of rain episodes take place only for some of the Patm-PW combinations, only these variables are unable to explain the rain occurrences because of not always they take place. This because a forecast sliding windows model with neural network was developed, to capture nonlinear relations that can not to be fully reflected by the lineal probabilistic ones based on the observed rains, Patm and PW series. This model stated a good correlation between the observed rains and the forecast, with a positive impact of the PW but negative of Patm. This model was able to predict the rain precipitation with a reasonable precision and reliable accuracy up to a 56 hours horizon.

Water vapor concentration structures in the atmosphere are well approximated horizontally by Gaussian random fields at small scales (6 km). These Gaussian random fields have a spatial correlation in accordance with a structure function with a two-thirds slope, following the corresponding law from Kolmogorov's theory of turbulence. This is proven by showing that the horizontal structure functions measured by several satellite instruments and radiosonde measurements do indeed follow the two-thirds law. High-spatialresolution retrievals of total column water vapor (TCWV) obtained from the Ocean and Land Color Instrument (OLCI) on board the Sentinel-3 series of satellites also qualitatively show a Gaussian random field structure. As a consequence, the atmosphere has an inherently stochastic component associated with the horizontal smallscale water vapor features, which, in turn, can make deterministic forecasting or nowcasting difficult. These results can be useful in areas where high-resolution modeling of water vapor is required, such as the estimation of the water vapor variance within a region or when searching for consistency between different water vapor measurements in neighboring locations. In terms of weather forecasting or nowcasting, the water vapor horizontal variability could be important in estimating the uncertainty of the atmospheric processes driving convection.

We evaluated the perfonnance of the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) at-Iaunch algorithm for monthly oceanic rain rate using six months (January-June 1998) of TMI data. Comparison with oceanic monthly rain rates derived from the Special Sensor Microwave Imager (SSM/I) data shows statistically significant differences. The TMI rain rates are lower than the SSM/I rain rates by about 10% overall, except for rain rates lower than 1.4 mm/day. The low TMI bias may be due to an overestimate of the columnar water vapor as indicated by the estimated rain layer thickness. The superior sampling by the TMI improves the algorithm statistics at the low rain rates. The averaged monthly rain rates over the latitudes between 40°S and 40~ are 2.78 and 3.17 mm/day, respectively for TMI and SSM/I, with RMS difference of 1.35 mm/day and correlation coefficient ofo.94. Sensor Microwave Imager (SSM/I) data (Chang and Chiu, 1999). As a standard TRMM product, data quality of this product must be assessed to allow usage by other researchers. In section 2 differences between the SSM/I and TMI sensor, platform, algorithm and data are discussed. Comparison results are presented in Section 3. Discussions are contained in Section 4.

ABSTRACTThe comparison between seven reanalyses – ERA‐Interim, National Centres for Environmental Predictions (NCEP)–National Center for Atmospheric Research reanalysis, NCEP Department of Energy reanalysis, NCEP–Climate Forecast System Reanalysis, Modern‐Era Retrospective Analysis for Research and Application (MERRA), MERRA‐2 and JRA‐55 – and assimilated radiosonde measurements was performed for eight radiosonde stations in the Baltic Sea region during 1980–2015 at five levels. In addition to raw radiosonde measurements, data sets corrected for temperature‐dependence (TD) and time‐lag errors in RS80‐A humidity measurements were investigated. While temperature differences between reanalyses and radiosonde stayed below 0.1 °C at the higher levels, differences up to 1.2 and 2.3 °C were revealed for the 2 m level at 00 UTC and 12 UTC, respectively. At the same time, biases in relative humidity increased with height. The new techniques assimilated in reanalyses were found to be in a dis...

Despite different techniques for the estimation of column integrated water vapour (precipitable water, PW) no method has yet been identified as the most accurate or the reference one. In this work we report intercomparisons between four PW estimation methods-radiosonde, Aerosol Robotic Network (AERONET), Global Positioning System (GPS), and High Resolution Limited Area Model (HIRLAM). Two intensive observation periods at Tõravere, Estonia, were used: 22 June−6 November 2008 and 9−12 August 2010. During the longer campaign, only observations by GPS, AERONET, and HIRLAM were performed. An agreement with average difference less than 2.2% among all three methods was established. However, compared to HIRLAM and GPS, the AERONET method overestimated PW by 5-9% at PW < 12 mm and underestimated it by 6-10% at PW > 25 mm. In addition, the consistency test applied indicated that previously reported uncertainty in AERONET-measured PW is too high. During the shorter but more complex campaign, data obtained with all four methods were available. Although the average differences between PW from radiosonde and three other methods were < 5%, the discrepancy between single measurements reached 33%. Relatively low temporal and spatial resolution of the HIRLAM grid as well as launching sparseness of radiosondes caused higher scatter from the other methods. The study suggests that besides radiosonde, as a traditional meteorological tool, the most reliable PW estimation can be made by GPS.

Les mesures issues de l&#39;interférométrie d&#39;images radar (InSAR), notamment satellitales, doivent être corrigées du délai dû à la propagation des ondes dans l&#39;atmosphère. Plusieurs travaux ont déjà montré l&#39;intérêt de cette correction. Cet article présente une méthode d&#39;estimation de la phase troposphérique qui utilise à la fois des mesures GNSS (Global Navigation Satellite System) et un modèle d&#39;atmosphère global (ERA-Interim). Pour évaluer cette méthode, nous comparons alors les mesures de déplacements interférométriques avant et après la correction avec les déplacements mesurés par GNSS, considérés comme référence. Les données utilisées pour les expériences sont acquises sur le Piton de la Fournaise en France.

Radar interferometry has proven to be a relevant technique in many application contexts. However, although the development of advanced processing, the interpretation of interferometric measurements is still disturbed by the presence of an atmospheric signal. In this work, we deal with the correction of interferograms using tropospheric delays measured from GNSS stations. We propose some experiments that enhance the influence of different factors in the processing of tropospheric maps. The results show that the interpolation strategy and the GNSS network geometry have a significant impact on the correction.

The estimation of the precipitable water vapor content (&lt;i&gt;W&lt;/i&gt;) with high temporal and spatial resolution is of great interest in both meteorological and climatological studies. Several methodologies based on remote sensing techniques have been recently developed, in order to obtain accurate and frequent measurements of this atmospheric parameter. Among them, the relative low cost and easy deployment of sun-sky radiometers, or sun-photometers, operating in several international networks, allowed the development of automatic estimations of &lt;i&gt;W&lt;/i&gt; from these instruments with high temporal resolution. However the great problem of this methodology is the estimation of the sun-photometric calibration parameters. The objective of this paper is to validate a new methodology based on the hypothesis that the calibration parameters characterizing the atmospheric transmittance at 940&thinsp;nm are dependent on vertical profiles of temperature, air pressure and moist...

The Urban Heat Island (UHI) effect occurs when the temperature in an urban area is higher than the temperature at a rural area. UHIs are monitored using remote sensing techniques such as satellite imagery or using temperature sensors deployed in a metropolitan area. In this chapter we propose a methodology to monitor the UHI intensity from Global Navigation Satellite Systems (GNSS) data. As the GNSS signal travels from the satellite to the receiver it propagates through the troposphere. A delay (Tropospheric delay) affects the signal. The delay is proportional to environmental variables. Also, the tropospheric delay in zenith direction (ZTD) is estimated as part of the Precise Point Positioning (PPP) technique. Therefore, in this chapter it is shown how to use process GNSS data to obtain ZTD and obtain temperature at an urban and a rural site simultaneously from the ZTD. The advantages of using GNSS data is its availability and many GNSS networks have been deployed in different citi...

We assess the performance of different break detection methods on three sets of benchmark data sets, each consisting of 120 daily time series of integrated water vapor differences. These differences are generated from the Global Positioning System (GPS) measurements at 120 sites worldwide, and the numerical weather prediction reanalysis (ERA-Interim) integrated water vapor output, which serves as the reference series here. The benchmark includes homogeneous and inhomogeneous sections with added nonclimatic shifts (breaks) in the latter. Three different variants of the benchmark time series are produced, with increasing complexity, by adding autoregressive noise of the first order to the white noise model and the periodic behavior and consecutively by adding gaps and allowing nonclimatic trends. The purpose of this "complex experiment" is to examine the performance of break detection methods in a more realistic case when the reference series are not homogeneous. We evaluate the performance of break detection methods with skill scores, centered root mean square errors (CRMSE), and trend differences relative to the trends of the homogeneous series. We found that most methods underestimate the number of breaks and have a significant number of false detections. Despite this, the degree of CRMSE reduction is significant (roughly between 40% and 80%) in the easy to moderate experiments, with the ratio of trend bias reduction is even exceeding the 90% of the raw data error. For the complex experiment, the improvement ranges between 15% and 35% with respect to the raw data, both in terms of RMSE and trend estimations. 1. Introduction Water vapor is a key component for the Earth's climate as it is the most important natural greenhouse gas and responsible for the largest known feedback mechanism for amplifying climate change (the water vapor feedback, see, for example, Soden & Held, 2006). Water vapor also strongly influences atmospheric dynamics and the hydrologic cycle through surface evaporation, latent heat transport and adiabatic heating, and is, in particular, a source of clouds and precipitation. Since the most important challenge of the present-day climate community is to predict and understand the response of the Earth system to increased emissions of greenhouse gases, an assessment of long-term trends in water vapor is vital. International efforts are made to collect, improve, and assess available water vapor measurements, for example, within the Global Energy and Water Exchanges (GEWEX) Water Vapor Assessment (http://gewex-vap.org) by the World Climate Research Programme (https://www.wcrp-climate.org/). However, due to its high variability, both temporally and spatially, water vapor is one of the most difficult quantities to measure and to predict with numerical weather prediction (NWP) models. Here, we consider the total vertical column water vapor measurements, often referred to as total column water vapor (TCWV), integrated water vapor (IWV), and precipitable water vapor (PWV). Those measurements can be

The total zenith tropospheric delay (ZTD) is an important parameter of the atmosphere which directly or indirectly give reflection of the atmospheric condition in a local GPS network. The use of the global tropospheric models such as the Saastamoinen model, Hopfield model, Neil model etc in estimating the tropospheric effects at the local level leaves much to be desired. These models are derived using data from available radiosonde obtained from Europe and North America continents. The global atmospheric condition used as constants in these models provides a broad approximation of the tropospheric conditions, but ignores the actual atmospheric conditions on a given location i.e. do not take into account the latitudinal and seasonal variations in the atmosphere. It is necessary to assess the effects of the troposphere on geodetic measurements based on ground meteorological measurements including temperature, pressure, and relative humidity. Daily RINEX GPS data from eight (8) Malaysian Real Time Kinematic GPS Network (MyRTKnet) stations in Southern Malaysia from year 2006 to June 2008 thus; covering 2½ years were processed. A computer program for modelling 2½ years of local meteorology within the network was developed and called MetMOD, similarly a program, call SaastroMOD was developed to estimate the local zenith hydrostatic delay. The results showed that, the estimated local tropospheric delay considered the temporal and spatial variation of the network thus; giving true reflection of the tropospheric delay in a local GPS network as against the standard atmosphere which ignores the actual local condition with a standard deviation of 0.18 m having a maximum zenith tropospheric delay (ZTD) of about 2.6 m. The best zenith hydrostatic delay (ZHD) comes from station JHJY with a standard deviation of 0.353 cm, while the best zenith wet delay (ZWD) comes from station TGRH with a standard deviation of 5.943 cm. The models show that, while parameters evolved for ZHD are constant at different locations, however, those parameters evolved for ZWD show significant variations from one location to the other. Hence then use of the local meteorological parameter in modelling tropospheric delay improves GPS positional accuracy.

In this paper, MSMR geophysical products like Integrated Water Vapour (IWV), Ocean Surface Wind Speed (OWS) and Cloud Liquid Water (CLW) in different grids of 50, 75 and 150 kms are compared with similar products available from other satellites like DMSP-SSM/I and TRMM-TMI. MSMR derived IWV, OWS and CLW compare well with SSM/I and TMI finished products. Comparison of MSMR derived CLW with that derived from TMI and SSM/I is relatively in less agreement. This is possibly due to the use of 37 GHz in SSM/I and TMI that is highly sensitive to CLW, while 37 GHz channels are not available on MSMR. Monthly comparison of MSMR geophysical products with those from TMI is all carried out for climatological purpose. The monthly comparisons were much better compared to instantaneous comparisons. In this paper, details of the data analysis and comparison results are presented. The usefulness of the MSMR vis-à-vis other sensors is also discussed.

The Cherenkov Telescope Array (CTA) in the southern hemisphere will be installed at Armazones 2k site in northern Chile. Scarce atmospheric observations are available in the region, particularly radiosonde data. This study analyzes radiosondes launched at Paranal observatory, located at about 21 km from the CTA site, from 24 October and 4 November 2011, to understand the behavior of density in the atmosphere near the CTA site. High-resolution numerical simulations with the Weather Research and Forecasting (WRF) model are validated with Paranal radiosondes to quantify its ability to represent the atmospheric conditions in the region. In addition, the seasonal and diurnal evolution of atmospheric density at the CTA site were studied during 2011 using the high-resolution weather forecasts from the WRF model.

We analyse the severe air quality episode in the Istanbul Metropolitan Area during 18-20 November 2009 using the HIRLAM and HARMONIE numerical weather prediction models and the HYSPLIT trajectory model. The HYSPLIT trajectory model result shows that the sources of this episode were local. The weather situation leading to this episode is analysed using meteorological observations. During the episode, a high pressure system over northwest Turkey, including Istanbul, led to the formation of an exceptionally strong ground-based temperature inversion which, combined with the low temperature and the heating, led to increased pollution levels.

The atmospheric precipitable water vapour content (PWV) strongly affects astronomical observations in the infrared (IR). We have validated the Weather Research and Forecasting (WRF) mesoscale numerical weather prediction (NWP) model as an operational forecasting tool for PWV. In the validation, we used atmospheric radiosounding data obtained directly at the Roque de los Muchachos Observatory [ORM: ≈2200 metres above sea level (masl)] during three intensive runs and an aditional verification sample of 1 yr of radiosonde data from World Meteorological Organization (WMO) station 60018 in Güímar (Tenerife, TFE: ≈105 masl). These data sets allowed us to calibrate the model at the observatory site and to validate it under different PWV and atmospheric conditions. The ability of the WRF model in forecasting the PWV at astronomical observatories and the effects of horizontal model grid size on the computed PWV and vertical profiles of humidity are discussed. An excellent agreement between model forecasts and observations was found at both locations, with correlations above 0.9 in all cases. Subtle but significant differences between model horizontal resolutions have been found, the 3 km grid size being the most accurate and the one selected for future work. Absolute calibrations are given for the lowest and finest grid resolutions. The median PWV values obtained were 3.8 and 18.3 mm at ORM and TFE, respectively. WRF forecasts will complement the PWV measured by the GPS monitoring system at the Canarian Observatories.

The METOP-A satellite Infrared Atmospheric Sounding Interferometer (IASI) Level 2 products comprise retrievals of vertical profiles of temperature and water vapor. The error covariance matrices and biases of the most recent version (4.3.1) of the L2 data were assessed, and the assessment was validated using radiosonde data for reference. The radiosonde data set includes dedicated and synoptic time launches at the Lindenberg station in Germany. For optimal validation, the linear statistical Validation Assessment Model (VAM) was used. The VAM uses radiosonde profiles as input and provides optimal estimate of the nominal IASI retrieval by utilizing IASI averaging kernels and statistical characteristics of the ensembles of the reference radiosondes. For temperatures above 900 mb and water retrievals above 700 mb, level expected and assessed errors are in good agreement. Below those levels, noticeable excess in assessed error is observed, possibly due to inaccurate surface parameters and undetected clouds/haze.

Modern Numerical Weather Prediction (NWP) models make use of the GNSS derived Zenith Total Delay (ZTD) or Integrated Water Vapour (IWV) estimates to enhance the quality of their forecasts. Usually, the ZTD is assimilated into the NWP models on hourly intervals but with the advancement of NWP models towards higher update rates, it has become necessary to estimate the ZTD on sub-hourly intervals. In turn, this imposes requirements related to the timeliness and accuracy of the ZTD estimates and has lead to a development of various strategies to process GNSS observations to obtain ZTD with different latencies and accuracies. Using present GNSS products and tools, ZTD can be estimated in real-time (RT), near real-time (NRT) and post-processing (PP) modes. The aim of this study is to provide an overview and accuracy assessment of various RT, NRT, and PP IWV estimation systems and comparing their achieved accuracy with the user requirements for GNSS meteorology. The NRT and PP systems are based on the Bernese GNSS Software v5.2 using a double-difference network and Precise Point Positioning (PPP) strategy, and the RT systems are based on BKG Ntrip Client 2.7 and PPP-Wizard both using PPP. One of the RT systems allows integer ambiguity resolution with PPP and therefore the effect of fixing integer ambiguities on ZTD estimates will also be presented. Discussion and Conclusions The four IWV and ZTD estimation systems at the University of Luxembourg have been introduced and their relative accuracy has been assessed by comparing them to the IGS Final Troposphere product. Mean differences of-0.7 ± 7.1 mm,-0.3 ± 4.7 mm, 10.2 ± 30.3 mm, and 60.4 ± 30.5 mm have been found for the Post-Processing, Near Real-Time, Real-Time I, and Real-Time II systems, respectively. Considering the averaged RMS difference between each solution and the IGS Final Troposphere product as a measure of its absolute accuracy, the achieved accuracies have been compared to GNSS meteorology user requirements for now-casting as outlined in COST Action 716. As a result of this comparison, it was found that the Post-Processing and the Near Real-Time systems meet the target requirements whereas both the real-time systems currently only meet the threshold requirements. The effect of integer ambiguity resolution on ZTD estimates was studied by using a modified version of the PPP-Wizard and it was found that the ambiguity fixed solution differs from the ambiguity float solution by less than a millimeter.

Since September 2011 the University of Luxembourg in collaboration with the University of Nottingham has been setting up two near real-time processing systems for ground-based GNSS data for the provision of zenith total delay (ZTD) and integrated water vapour (IWV) estimates. Both systems are based on Bernese v5.0, use the double-differenced network processing strategy and operate with a 1-hour (NRT1h) and 15-minutes (NRT15m) update cycle. Furthermore, the systems follow the approach of the E-GVAP METO and IES2 systems in that the normal equations for the latest data are combined with those from the previous four updates during the estimation of the ZTDs. NRT1h currently takes the hourly data from over 130 GNSS stations in Europe whereas NRT15m is primarily using the real-time streams of EUREF-IP. Both networks include additional GNSS stations in Luxembourg, Belgium and France. The a priori station coordinates for all of these stem from a moving average computed over the last 20 to ...

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

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This study assesses the performance of the Weather Research and Forecasting (WRF) model to represent the near-surface weather conditions and the precipitable water vapour (PWV) in the Chajnantor plateau, in the north of Chile, from 2007 April to December. The WRF model shows a very good performance forecasting the near-surface temperature and zonal wind component, although it overestimates the 2 m water vapour mixing ratio and underestimates the 10 m meridional wind component. The model represents very well the seasonal, intraseasonal and the diurnal variation of PWV. However, the PWV errors increase after the 12 h of simulation. Errors in the simulations are larger than 1.5 mm only during 10 per cent of the study period, they do not exceed 0.5 mm during 65 per cent of the time and they are below 0.25 mm more than 45 per cent of the time, which emphasizes the good performance of the model to forecast the PWV over the region. The misrepresentation of the near-surface humidity in the region by the WRF model may have a negative impact on the PWV forecasts. Thus, having accurate forecasts of humidity near the surface may result in more accurate PWV forecasts. Overall, results from this, as well as recent studies, supports the use of the WRF model to provide accurate weather forecasts for the region, particularly for the PWV, which can be of great benefit for astronomers in the planning of their scientific operations and observing time.

Varios observatorios astronómicos operan actualmente en Chile. Estos necesitan pronósticos atmosféricos exactos con días de anticipación para optimizar la planificación de observaciones y reducir costos de operación. El modelo GFS (Global Forecast System) produce pronósticos atmosféricos cuatro veces al día y sus salidas están disponibles gratis en internet. En este trabajo se presentan resultados preliminares de la estimación y pronóstico de propiedades atmosféricas en los observatorios APEX y Paranal. El modelo GFS muestra una buena correspondencia con observaciones en la predicción de PWV (precipitable water vapor) en APEX, particularmente en el rango entre 0-1 mm usando el PWV calculado a partir de perfiles de razón de mezcla de vapor de agua (q v). El modelo puede ser también usado con cierta confianza para predecir variables cerca del suelo en observatorios con el uso de un filtro de Kalman. El PWV estimado a partir de datos de GOES pudiera ser usado como método diagnóstico aunque se recomienda mejor usar modelos numéricos siempre que estén disponibles.

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

The Microwave Radiometers (MWR) on-board ERS-1, ERS-2, and Envisat provide a continuous time series of brightness temperature observations between 1991 and 2012. Here we report on a new Total Column Water Vapour (TCWV) and Wet Tropospheric Correction (WTC) dataset that builds on this time series. We use a one-dimensional variational approach to derive TCWV from MWR observations and ERA-Interim background information. A particular focus of this study lies on the intercalibration of the three different instruments, which is performed using constraints on liquid water path (LWP) and TCWV. Comparing our MWR-derived time series of TCWV against TCWV derived from Global Navigation Satellite System (GNSS) we find that the MWR-derived TCWV time series is stable over time. However, observations potentially affected by precipitation show a degraded performance compared to precipitation-free observations in terms of the accuracy of retrieved TCWV. An analysis of WTC shows further that the retri...

A tmospheric water vapor is the dominant greenhouse gas in the earth's atmosphere, and so quantifying the feedback of water vapor in global warming is therefore of paramount importance. This feedback is also crucial for other feedback processes, such as snow-sea ice and clouds, that also play a significant role in climate. The lack of detailed knowledge of the hydrological cycle thus is a major limiting factor toward a better understanding of the earth's climate system. The inaccuracy is substantial and concerns practically all aspects of the hydrological cycle. At present, even such a basic quantity as the global annual precipitation rate is probably only possible to be determined to an accuracy of some 10% (Adler et al. 2001). The main reason for this appears to be the high spatial and temporal variability of the water cycle, for which the sampling properties of current observing systems are wholly inadequate. To find new and cost-efficient ways to improve the global observing system is a central objective for the weather and climate prediction communities. Global Positioning System (GPS)-based measurements offer here new and promising possibilities. One of these is the capability to provide data at similar

HARMONIE upper-air humidity data assimilation The HARMONIE km-scale forecasting system is running daily at several HIRLAM institutes. The main components are the data assimilation system and the forecast model. The data assimilation system consists of one for surface properties and one for upper-air. The default upper-air data assimilation is based on 3-dimensional variational data assimilation, with conventional types of observations assimilated with a 3 h data assimilation cycle. Multivariate background error statistics are derived from ECMWF Ensemble Data Assimilation (EDA), downscaled with the HARMONIE kmscale model. The only direct humidity measurements are provided by vertical profile measurements from radiosondes and 2 m relative humidity measurements from SYNOP stations. Since an accurate initial humidity model state is important for short range prediction of detailed weather, an enhanced usage of humidity observations is aimed for.

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We present a practical method to continuously calibrate Raman lidar observations of water vapor mixing ratio profiles. The water vapor profile measured with the multiwavelength polarization Raman lidar Polly XT is calibrated by means of co-located AErosol RObotic NETwork (AERONET) sun photometer observations and Global Data Assimilation System (GDAS) temperature and pressure profiles. This method is applied to lidar observations conducted during the Cyprus Cloud Aerosol and Rain Experiment (CyCARE) in Limassol, Cyprus. We use the GDAS temperature and pressure profiles to retrieve the water vapor density. In the next step, the precipitable water vapor from the lidar observations is used for the calibration of the lidar measurements with the sun photometer measurements. The retrieved calibrated water vapor mixing ratio from the lidar measurements has a relative uncertainty of 11 % in which the error is mainly caused by the error of the sun photometer measurements. During CyCARE, nine measurement cases with cloud-free and stable meteorological conditions are selected to calculate the precipitable water vapor from the lidar and the sun photometer observations. The ratio of these two precipitable water vapor values yields the water vapor calibration constant. The calibration constant for the Polly XT Raman lidar is 6.56 g kg −1 ± 0.72 g kg −1 (with a statistical uncertainty of 0.08 g kg −1 and an instrumental uncertainty of 0.72 g kg −1). To check the quality of the water vapor calibration, the water vapor mixing ratio profiles from the simultaneous nighttime observations with Raman lidar and Vaisala radiosonde sounding are compared. The correlation of the water vapor mixing ratios from these two instruments is determined by using all of the 19 simultaneous nighttime measurements during CyCARE. Excellent agreement with the slope of 1.01 and the R 2 of 0.99 is found. One example is presented to demonstrate the full potential of a well-calibrated Raman lidar. The relative humidity profiles from lidar, GDAS (simulation) and radiosonde are compared, too. It is found that the combination of water vapor mixing ratio and GDAS temperature profiles allow us to derive relative humidity profiles with the relative uncertainty of 10-20 %. 1 Introduction Water vapor has a large impact on the thermodynamic state of the atmosphere and is the most important greenhouse gas (Twomey, 1991). The relative humidity (RH) represents the water vapor content of an atmospheric volume and is also one of the most important atmospheric state parameters. RH has a strong influence on visibility and the optical properties of atmospheric particles. However, due to the spatiotemporal variability of the water vapor, it is difficult to consider the water vapor in weather forecasts and climate models in an appropriate manner (Held and Soden, 2000; Tompkins, 2002). Hence, water vapor observations by vertical profiling instrumentation with high spatiotemporal resolution will support Published by Copernicus Publications on behalf of the European Geosciences Union.

SUMMARY The basic idea of this study is the estimation of Tropospheric delay with all of possible observations and methods available in the surround area of EUREF GPS station (AUT1) of the Aristotle University of Thessaloniki. The tropospheric delay is estimated in situ from three different data sources. We estimate the tropospheric delay using 3 days of GPS pseudorange observations (days 157, 158, 159 in 2005), and the Total Zenith delay derived from the EUREF analysis centre. We compare the estimated delay from the permanent GPS data with that measured from radiosonde measurements which carried out at Thessaloniki airport, located near to GPS station. In addition, the tropospheric delay is estimated using three known tropospheric models, Ifadis, Niell and Saastamoinen. The tropospheric delay from Ifadis and Niell models is computed using the corresponding mapping functions with the calculated Total Zenith delay from radiosonde data. In Saastamoinen model surface meteorological dat...

To assess current remote-sensing capabilities for wind energy applications, a remote-sensing system evaluation study, called XPIA (eXperimental Planetary boundary layer Instrument Assessment), was held in the spring of 2015 at NOAA’s Boulder Atmospheric Observatory (BAO) facility. Several remote-sensing platforms were evaluated to determine their suitability for the verification and validation processes used to test the accuracy of numerical weather prediction models. &lt;br&gt;&lt;br&gt; The evaluation of these platforms was performed with respect to well-defined reference systems: the BAO’s 300-m tower equipped at 6 levels (50, 100, 150, 200, 250, and 300&thinsp;m) with 12 sonic anemometers and 6 temperature and relative humidity sensors; and approximately 60 radiosonde launches. &lt;br&gt;&lt;br&gt; In this study we first employ these reference measurements to validate temperature profiles retrieved by two co-located microwave radiometers, as well as virtual temperature measured ...

We thank the reviewer for his helpful and contructive comments. With respect to additional technical information: we refer to the Frech et al. 2013 where details of the DWD radar system (including the antenna assembly) are introduced. With respect to the question on the findings in Hubbert (2107): Hubbert (2017) also showed a Zdr bias that depended on the temperature of the antenna assembly. This physically designates everything to the right of the reference plane in Fig. (1). For S-Pol this encompases the waveguide out of a sea container, up

In this paper, MSMR geophysical products like Integrated Water Vapour (IWV), Ocean Surface Wind Speed (OWS) and Cloud Liquid Water (CLW) in different grids of 50, 75 and 150 kms are compared with similar products available from other satellites like DMSP-SSM/I and TRMM-TMI. MSMR derived IWV, OWS and CLW compare well with SSM/I and TMI finished products. Comparison of MSMR derived CLW with that derived from TMI and SSM/I is relatively in less agreement. This is possibly due to the use of 37 GHz in SSM/I and TMI that is highly sensitive to CLW, while 37 GHz channels are not available on MSMR. Monthly comparison of MSMR geophysical products with those from TMI is all carried out for climatological purpose. The monthly comparisons were much better compared to instantaneous comparisons. In this paper, details of the data analysis and comparison results are presented. The usefulness of the MSMR vis-à-vis other sensors is also discussed.

This paper presents a conceptual approach for near real time Global Positioning System (GPS) meteorology in Malaysia using combined space-and ground-based GPS observations. Data from a single GPS station is utilised to derive wet refractivities for comparison with radio occultation (RO). This study shows that the wet refractivities from the ground-based GPS present similar patterns and better correlation with the radiosonde data than that from the space-based GPS RO data at altitudes between 0-5 km. Similarly, the wet refractivities derived from RO are more highly correlated with the radiosonde data than the ground-based GPS at altitudes above 5 km. The residual N w vary from-9.25-21.136 N-unit at 00 h UTC for the ground-based GPS while for the GPS RO, it is-19-9.259 N-unit at 00 h UTC.

The local distribution of water vapour in the urban area of Rome has been studied using both a high resolution mesoscale model (MM5) and Earth Remote Sensing-1 (ERS-1) satellite radar data. Interferometric Synthetic Aperture Radar (InSAR) techniques, after the removal of all other geometric effects, estimate excess path length variation between two different SAR acquisitions (Atmospheric Phase Screen: APS). APS are strictly related to the variations of the water vapour content along the radar line of sight. To the aim of assessing the MM5 ability to reproduce the gross features of the Integrated Water Vapour (IWV) spatial distribution, as a first step ECMWF IWV has been used as benchmark against which the high resolution MM5 model and InSAR APS maps have been compared. As a following step, the high resolution IWV MM5 maps have been compared with both InSAR and surface meteorological data. The results show that the high resolution IWV model maps compare well with the InSAR ones. Support to this finding is obtained by semivariogram analysis that clearly shows good agreement beside from a model bias.

A field campaign was carried out in the framework of the Mitigation of Electromagnetic Transmission errors induced by Atmospheric Water Vapour Effects (METAWAVE) project sponsored by the European Space Agency (ESA) to investigate the accuracy of currently available sources of atmospheric columnar integrated water vapor measurements. The METAWAVE campaign took place in Rome, Italy, for the 2-week period from 19 September to 4 October 2008. The collected dataset includes observations from groundbased microwave radiometers and Global Positioning System (GPS) receivers, from meteorological numerical model analysis and predictions, from balloon-borne in-situ radiosoundings, as well as from spaceborne infrared radiometers. These different sources of integrated water vapor (IWV) observations have been analyzed and compared to quantify the accuracy and investigate the potential for mitigating IWVrelated electromagnetic path delay errors in Interferometric Synthetic Aperture Radar (InSAR) imaging. The results, which include a triple collocation analysis accounting for errors inherently present in every IWV measurements, are valid not only to InSAR but also to any other application involving water vapor sensing. The present analysis concludes that the requirements for mitigating the effects of turbulent water vapor component into InSAR are significantly higher than the accuracy of the instruments analyzed here. Nonetheless, information on the IWV vertical stratification from satellite observations, numerical models, and GPS receivers may provide valuable aid to suppress the long spatial wavelength (>20 km) component of the atmospheric delay, and thus significantly improve the performances of InSAR phase unwrapping techniques.

The local distribution of water vapour in the urban area of Rome has been studied using both a high resolution mesoscale model (MM5) and Earth Remote Sensing-1 (ERS-1) satellite radar data. Interferometric Synthetic Aperture Radar (InSAR) techniques, after the removal of all other geometric effects, estimate excess path length variation between two different SAR acquisitions (Atmospheric Phase Screen: APS). APS are strictly related to the variations of the water vapour content along the radar line of sight. To the aim of assessing the MM5 ability to reproduce the gross features of the Integrated Water Vapour (IWV) spatial distribution, as a first step ECMWF IWV has been used as benchmark against which the high resolution MM5 model and InSAR APS maps have been compared. As a following step, the high resolution IWV MM5 maps have been compared with both InSAR and surface meteorological data. The results show that the high resolution IWV model maps compare well with the InSAR ones. Support to this finding is obtained by semivariogram analysis that clearly shows good agreement beside from a model bias.

The spatial and temporal variations of atmospheric precipitable water (PW) content anomalies were analysed over Europe from 1973 to 2003 using daily data (0000 and 1200 UTC) from National Center of Environmental Prediction and National Center of Atmospheric Research Reanalysis project (NCEP-1) and in situ radiosonde data. Mann-Kendall trend tests were applied to long-term PW time series. Technology changes influence PW radiosonde trends, although these are in agreement with NCEP-1 trends. Over the south of the Iberian Peninsula, trends are negative and statistically significant (<−0.04 mm year −1 ; p < 0.05) and positive over the Central European Mountains (The Alpes) and the North Atlantic Ocean (>0.04 mm year −1 ; p < 0.05). Seasonal trends revealed negative and significant trends over the Iberian Peninsula for all seasons (<−0.03 mm year −1 ; p < 0.05).

Retrieval of land surface variables and atmospheric variables over land from passive microwave remote-sensing data sets has been a challenge for many years. A lot of progress has been made in these quests such as using cloud-resolving models and data assimilation. Data assimilation allows the integration of observations (including observation errors) into imperfect models, thereby yielding more improved model forecasts. In this work, a coupled data assimilation framework (CDAF) is proposed and applied to predict the evolution of land surface and atmospheric conditions. CDAF comprises a coupling of two data assimilation schemes, namely a land data assimilation scheme (LDAS) and an ice microphysics data assimilation scheme (IMDAS). This system has been developed and evaluated using data for the Tibetan Plateau. In this framework, both low-frequency and high-frequency passive microwave brightness temperatures (T B s) are assimilated. Low-frequency T B s are assimilated in the LDAS subsystem and used to obtain land surface conditions, which are subsequently used as improved initial conditions together with high-frequency T B s and assimilated in the IMDAS subsystem to obtain atmospheric conditions. The retrieved land surface variables and integrated atmospheric variables are demonstrated to show good agreement with observed land and atmosphere conditions such as those derived from point measurements of temperature and soil moisture (using the Soil Moisture and Temperature Measurement System (SMTMS)), sonde, Advanced Infrared Sounder (AIRS) and Global Precipitation Climatology Project (GPCP) products. The distribution of integrated cloud liquid water and cloud ice is shown to follow the observed cloud distribution over the study area. It is shown that by using IMDAS with modifications to account for precipitation and a good description of land surface emission, it is possible to obtain precipitation information of high fidelity over the land surface. Retrieved integrated water vapour using IMDAS shows correspondence with 'corrected' AIRS total precipitable water product. It is also shown that the relative humidity profile obtained from IMDAS agrees with the corresponding sonde profile. From the simulations, it is clear that by using the CDAF, there is marked improvement in the forecast conditions compared with the non-assimilation scenario for all of

Carbon dioxide is generally regarded as the most important greenhouse gas affecting global warming. Many researches have been conducted to measure atmospheric CO 2 concentration, analyze CO 2 variation on both seasonal and interannual scales and predict future CO 2 tendencies. Among them, ground-based remote sensing observation of CO 2 is the essential approach to provide validation data for satellite observation owning to its much higher accuracy and column CO 2 measurement capability. Unlike the Fourier Transform Spectrometer, the sun photometer observation system introduced in this paper takes advantages of full-automation, easily portable and mobility to provide a non-supervised automatic field operational CO 2 observation approach. In this study we acquired the CO 2 measurements from 2010 to 2012 in Beijing, translated them to cloud free data, and designed an index related to column CO 2 amount. The diurnal and seasonal variations of atmospheric CO 2 acquired by the system were also analyzed. We compared our observation with the simulation of CarbonTracker model, and good agreements with the model suggested the long-term stability of the system and reasonability of the data processing method. The future work will focus on absolute CO 2 quantity retrieval and accuracy assessment of this observation system.

Водната пара е най-разпространеният парников газ и се очаква да нараства в атмосферата вследствие на глобалното затопляне. Поради големите си времеви и пространствени нееднородности водната пара е трудна за измерване със стандартните метеорологични уреди. През последните 15 години се наложи нов метод за измерване на интегрираното във височина количество водна пара в атмосферата с помощта на глобалните навигационни спътникови системи (ГНСС), а именно ГНСС метеорология. В Европа методът е широко разпространен, като в момента Националните метеорологични институти получават данни от повече от 1600 наземни ГНСС станции чрез проекта на EUMETNET-E-GVAP. В югоизточна Европа този метод е слабо познат и все още не се използва. Тук за първи път се разглежда денонощният ход на интегрираната по височина водна пара за 8 станции в югоизточна Европа по време на топлинната вълна между 19-ти и 25-ти юли 2007 година. В станциите, разположени край Черно море – Констанца и Варна, водната пара има максим...

The local distribution of water vapour in the urban area of Rome has been studied using both a high resolution mesoscale model (MM5) and Earth Remote Sensing-1 (ERS-1) satellite radar data. Interferometric Synthetic Aperture Radar (InSAR) techniques, after the removal of all other geometric effects, estimate excess path length variation between two different SAR acquisitions (Atmospheric Phase Screen: APS). APS are strictly related to the variations of the water vapour content along the radar line of sight. To the aim of assessing the MM5 ability to reproduce the gross features of the Integrated Water Vapour (IWV) spatial distribution, as a first step ECMWF IWV has been used as benchmark against which the high resolution MM5 model and InSAR APS maps have been compared. As a following step, the high resolution IWV MM5 maps have been compared with both InSAR and surface meteorological data. The results show that the high resolution IWV model maps compare well with the InSAR ones. Support to this finding is obtained by semivariogram analysis that clearly shows good agreement beside from a model bias.

The estimation of the precipitable water vapor content (&lt;i&gt;W&lt;/i&gt;) with high temporal and spatial resolution is of great interest in both meteorological and climatological studies. Several methodologies based on remote sensing techniques have been recently developed, in order to obtain accurate and frequent measurements of this atmospheric parameter. Among them, the relative low cost and easy deployment of sun-sky radiometers, or sun-photometers, operating in several international networks, allowed the development of automatic estimations of &lt;i&gt;W&lt;/i&gt; from these instruments with high temporal resolution. However the great problem of this methodology is the estimation of the sun-photometric calibration parameters. The objective of this paper is to validate a new methodology based on the hypothesis that the calibration parameters characterizing the atmospheric transmittance at 940&thinsp;nm are dependent on vertical profiles of temperature, air pressure and moist...