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In recent years, precipitable water vapor has emerged as a key atmospheric parameter in weather prediction research. However, relying on only one parameter does not always accurately predict rainfall, as other atmospheric parameters also contribute to initiating rain events. In our previous study, we explored a methodology for rainfall forecasting based on the atmospheric delay gradient at an individual station in the tropical region. Commonly referred to as the atmospheric gradient, it accounts for the delay in GNSS signals due to changes in horizontal refractivity while propagating through the troposphere to the ground station. This paper discusses the spatial and temporal correlation of the gradient with precipitation occurring over a region. We have investigated different features, such as gradient magnitude, gradient convergence, and the flux of the atmospheric gradient, to propose potential nowcasting criteria for precipitation in the tropical climatic zone. The atmospheric gradient in the surrounding region orients toward the area of precipitation as the rain event approaches. This gradient gradually alters its orientation and converges toward the region of rainfall at the time of precipitation, introducing the concept of gradient convergence. This phenomenon results in an inward gradient flux at the time of the downpour, which presents a potential parameter for rainfall prediction over an area. We also proposed investigating area sizes of 4° × 4° or 8° × 8°, depending on the gradient feature, to establish a forecasting methodology for rainfall. The weather front in the tropical region begins to initiate 12 h before the actual time of occurrence and advances toward the area where it will have an impact from a more distant location. This article presents a detailed investigation of various features of the atmospheric gradient and its potential to nowcast rainfall for a region, as well as proposes the lead time for long-term weather predictions. Furthermore, a deep neural network has been employed to predict rainfall events for the next 6 h over an area in the tropical region. [ABSTRACT FROM AUTHOR]

In space geodetic techniques, the mapping functions (MFs) provide the relationship between zenith and slant tropospheric delays. The MFs are determined under the assumption of spherically layered atmosphere. However, the atmosphere is not spherically layered, and the asymmetry should be considered. Therefore, tropospheric gradients are taken into account. Nevertheless, tropospheric gradients alone can not fully represent the deviation from a spherically layered atmosphere, and hence cm level errors arise especially for low elevation angles. In this study, we present new approaches to modify the wet MF to reduce mismodelling of tropospheric delays. The delays in the study were calculated using ray-tracing algorithm based on ECMWF's ERA5 dataset. We first analyzed the performances of the new approaches. Then, two Precise Point Positioning (PPP) simulation studies and a real case study were carried out for two different regions namely Germany and Türkiye. According to the results, the proposed approaches reduce the modelling errors up to by a factor 6 for both regions. Besides, simulation studies show that the approaches improve the accuracies of the ZTDs and heights. In the practical application however, we could not find a clear improvement in the PPP analyze and this might be related to the ERA5 which can not be regarded error-free. [ABSTRACT FROM AUTHOR]

Accurate modeling of tropospheric delays is crucial for the global navigation satellite system (GNSS), which finds extensive applications in early warning systems of natural hazards and extreme weather forecasting. Zenith tropospheric delay (ZTD) is estimated as a random walk process with a constraint in GNSS processing. The constraint, referred to as random walk process noise (RWPN), holds significant importance in real-time ZTD estimation and exhibits geographical and temporal specificity. Presently, RWPN is treated as either a constant value or derived from a numerical weather model (NWM). To address this, our study presents a global RWPN model (GRM) by parameterizing a decade of NWM-derived RWPN data. Taking into account its spatiotemporal nature, we formulate the RWPN equation for each station by employing trigonometric, exponential, and Legendre functions. The optimum RWPN value is determined by incorporating GRM using latitude, longitude, orthometric height, and time as inputs. To validate the efficacy of GRM, we compare its performance against RWPN values derived from both JRA-55 and ERA5 datasets for the year 2020. The results indicate that the GRM-derived values exhibit enhanced accuracy in comparison with the optimal fixed RWPN values, as well as the yearly and monthly mean RWPN values. Additionally, we assess the efficacy of the GRM model in real-time ZTD estimation across 20 globally distributed GNSS stations. The results reveal an improvement exceeding 10% when compared to the results of the best fixed RWPN values. The GRM model offers an effective solution for obtaining accurate RWPN values on a global level, all while minimizing computational demands and time constraints. This notable progress significantly bolsters the precision of real-time GNSS estimates, thus facilitating their application in time-sensitive geophysical and meteorological scenarios. Highlights: A global RWPN model is introduced for real-time GNSS tropospheric delay estimation. The model provides precise RWPN values while minimizing computation cost and time. The proposed model improves the accuracy of real-time ZTD estimation by over 10%. This model promotes GNSS in time-critical geophysical applications. [ABSTRACT FROM AUTHOR]

Heavy rainfall events during the Indian summer monsoon cause landslides and flash floods resulting in a significant loss of life and property every year. The exactness of the model physics representation and initial conditions is critical for accurately predicting these events using a numerical weather model. The values of parameters in the physics schemes influence the accuracy of model prediction; hence, these parameters are calibrated with respect to observation data. The present study examines the influence of hybrid data assimilation on a parameter-calibrated WRF model. Twelve events during the period 2018-2020 were simulated in this study. Hybrid assimilation on the WRF model significantly reduced the model prediction error of the variables: rainfall (18.04%), surface air temperature (7.91%), surface air pressure (5.90%) and wind speed at 10 m (27.65%) compared to simulations with default parameters without assimilation. [ABSTRACT FROM AUTHOR]

In this research, three-dimensional and four-dimensional tomography is used to demonstrate the distribution of wet refractivity index of the troposphere. In this model, spherical cap harmonics are used for the horizontal distribution of the wet refractivity index, and empirical orthogonal functions are used for the vertical distribution of the index. The region of study is in the west California State, and the wet refractivity index is retrieved from the wet tropospheric delay measurements. to validate the results, radiosonde profiles were compared to the tomographically retrieved profiles. The result shows that wet refractivity indices can be retrieved using functional models with RMSE about 2.4 ppm till 3.9 in four-dimension method. The comparisons show that the four-dimensional retrieved profiles shows improvement up to 34 and 42 percentage in mid-day tomography epochs compare to three-dimensional tomography results. Also it can be seen that in mid-night epochs three-dimensional tomography has higher accuracy compare to four-dimension method because of low variation of wet refractivity indices

This paper introduces a technique to transform between geometric and barometric estimates of altitude and vice-versa. Leveraging forecast numerical weather models, the method is unbiased and has a vertical error with a standard deviation of around 30 m (100 ft), regardless of aircraft altitude, which makes it significantly more precise than established comparable conversion functions. This result may find application in various domains of civil aviation, including vertical RNP, systemized airspace, and automatic landing systems. [ABSTRACT FROM AUTHOR]

Variations in the water vapor that atmospheric rivers (ARs) carry toward North America within Pacific storms strongly modulates the spatiotemporal distribution of west‐coast precipitation. The "AR Recon" program was established to improve forecasts of landfalling Pacific‐coast ARs and their associated precipitation. Dropsondes are deployed from weather reconnaissance aircraft and pressure sensors have been added to drifting ocean buoys to fill a major gap in standard weather observations, while research is being conducted on the potential for airborne Global Navigation Satellite System (GNSS) radio occultation (ARO) to also contribute to forecast improvement. ARO further expands the spatial coverage of the data collected during AR Recon flights. This study provides the first description of these data, which provide water vapor and temperature information typically as far as 300 km to the side of the aircraft. The first refractivity profiles from European Galileo satellites are provided and their accuracy is evaluated using the dropsondes. It is shown that spatial variations in the refractivity anomaly (difference from the climatological background) are modulated by AR features, including the low‐level jet and tropopause fold, illustrating the potential for RO measurements to represent key AR characteristics. It is demonstrated that assimilation of ARO refractivity profiles can influence the moisture used as initial conditions in a high‐resolution model. While the dropsonde measurements provide precise, in situ wind, temperature and water vapor vertical profiles beneath the aircraft, and the buoys provide surface pressure, ARO provides complementary thermodynamic information aloft in broad areas not otherwise sampled at no additional expendable cost. Plain Language Summary: Aircraft are deployed to make extraobservations and improve forecasts of the atmospheric rivers that carry moisture toward the coast of North America in northeast Pacific storms. Dropsondes are released from the aircraft to measure moisture, temperature and winds as they descend. We have increased the coverage of the aircraft measurements by making simultaneous measurements of the atmosphere using Global Positioning System (GPS) signals, and for the first time, signals from the European Galileo satellite system which provides 50% more radio occultation measurements. These airborne radio occultation (ARO) measurements of the refractive index of the atmosphere are based on travel time delays of the satellite signals, and are used to provide refractivity, moisture, and temperature profile information to the sides of the aircraft. The observations are assimilated into a mesoscale weather model to improve the initial moisture conditions used for the weather forecast. Cross sections through the updated model volume show variations in the refractivity that correspond to the atmospheric river features at low levels and cold front features at upper levels. Key Points: Flights over northeast Pacific atmospheric rivers provide dense airborne radio occultation and dropsonde data for assimilation in modelsThe first Galileo RO profiles are compared with nearby Global Positioning System (GPS) profiles to assess accuracy in the troposphereThe model refractivity anomaly distinguishes key characteristics of the atmospheric river including the low‐level jet and tropopause fold [ABSTRACT FROM AUTHOR]

This paper presents the first experimental results of a study on the ingestion in the Weather Research and Forecasting (WRF) model, of Sentinel satellites and Global Navigation Satellite Systems (GNSS) derived products. The experiments concern a flash-floodevent occurred in Tuscany (Central Italy) in September 2017. The rationale is that numerical weather prediction (NWP) models are presently able to produce forecasts with a km scale spatial resolution, but the poor knowledge of the initial state of the atmosphere may imply an inaccurate simulation of the weather phenomena. Hence, to fully exploit the advances in numerical weather modelling, it is necessary to feed them with high spatiotemporal resolution information over the surface boundary and the atmospheric column. In this context, the Copernicus Sentinel satellites represent an important source of data, because they can provide a set of high-resolution observations of physical variables (e.g. soil moisture, land/sea surface temperature, wind speed) used in NWP models runs. The possible availability of a spatially dense network of GNSS stations is also exploited to assimilate water vapour content. Results show that the assimilation of Sentinel-1 derived wind field and GNSS-derivedwater vapour data produce the most positive effects on the performance of the forecast.

In GNSS analysis, the tropospheric delay is parameterized by applying mapping functions (MFs), zenith delays, and tropospheric gradients. Thereby, the wet and hydrostatic MF are derived under the assumption of a spherically layered atmosphere. The coefficients of the closed-form expression are computed utilizing a climatology or numerical weather model (NWM) data. In this study, we analyze the impact of tropospheric mismodelling on estimated parameters in precise point positioning (PPP). To do so, we mimic PPP in an artificial environment, i.e., we make use of a linearized observation equation, where the observed minus modelled term equals ray-traced tropospheric delays from a high-resolution NWM. The estimated parameters (station coordinates, clocks, zenith delays, and tropospheric gradients) are then compared with the known values. The simulation study utilized a cut-off elevation angle of 3° and the standard downweighting of low elevation angle observations. The results are representative of a station located in central Europe and the warm season. In essence, when climatology is utilized in GNSS analysis, the root mean square error (RMSE) of the estimated zenith delay and station up-component equal about 2.9 mm and 5.7 mm, respectively. The error of the GNSS estimates can be reduced significantly if the correct zenith hydrostatic delay and the correct hydrostatic MF are utilized in the GNSS analysis. In this case, the RMSE of the estimated zenith delay and station up-component is reduced to about 2.0 mm and 2.9 mm, respectively. The simulation study revealed that the choice of wet MF, when calculated under the assumption of a spherically layered troposphere, does not matter too much. In essence, when the ‘correct’ wet MF is utilized in the GNSS analysis, the RMSE of the estimated zenith delay and station up-component remain at about 1.8 mm and 2.4 mm, respectively. Finally, as a by-product of the simulation study, we developed a modified wet MF, which is no longer based on the assumption of a spherically layered atmosphere. We show that with this modified wet MF in the GNSS analysis, the RMSE of the estimated zenith delay and station up-component can be reduced to about 0.5 mm and 1.0 mm, respectively. In practice, its success depends on the ability of current (future) NWM to predict the fourth coefficient of the developed closed-form expression. We provide some evidence that current NWMs are able to do so.

We are discussing changepoint detection in tropospheric parameter time series that occurs in a numerical weather reanalysis model. Our approach applies a statistical method that is based on the maximum value of two sample t-statistics. We use critical values calculated by applying an asymptotic distribution. We also apply an asymptotic distribution to finding approximate critical values for the changepoint position. Experiments on "test" and "real" data illustrate the assumed accuracy and efficiency of our method. The method is assessed by its application to our series after adding synthetic shifts. A total of more than 3,000 original profiles are then analysed within the time-span of the years 1990-2015. The analysis shows that at least one changepoint is present in more than 9% of the studied original time series. The uncertainty of estimated times achieved tens of days for shifts larger than 9 mm, but it was increased up to hundreds of days in the case of smaller synthetic shifts. Discussed statistical method has potential for suspected change point detection in time series with higher time resolution. [ABSTRACT FROM AUTHOR]

This paper presents the first experimental results of a study on the ingestion in the Weather Research and Forecasting (WRF) model, of Sentinel satellites and Global Navigation Satellite Systems (GNSS) derived products. The experiments concern a flash-floodevent occurred in Tuscany (Central Italy) in September 2017. The rationale is that numerical weather prediction (NWP) models are presently able to produce forecasts with a km scale spatial resolution, but the poor knowledge of the initial state of the atmosphere may imply an inaccurate simulation of the weather phenomena. Hence, to fully exploit the advances in numerical weather modelling, it is necessary to feed them with high spatiotemporal resolution information over the surface boundary and the atmospheric column. In this context, the Copernicus Sentinel satellites represent an important source of data, because they can provide a set of high-resolution observations of physical variables (e.g. soil moisture, land/sea surface temperature, wind speed) used in NWP models runs. The possible availability of a spatially dense network of GNSS stations is also exploited to assimilate water vapour content. Results show that the assimilation of Sentinel-1 derived wind field and GNSS-derivedwater vapour data produce the most positive effects on the performance of the forecast. [ABSTRACT FROM AUTHOR]

We correct some errors and give some more research for our paper published in Survey Review, 2016, 48(351), 385–391. This note is a correction owing to the use of the questionable surface meteorological measurements of the International GNSS Service (IGS) and also gives more sufficient studies to validate our method due to our inadequate study. Numerical weather model values of surface meteorological data were used to check the IGS surface meteorological measurements and long-time zenith tropospheric delay values at 76 radiosonde stations in Russia and near Russia were added as test data. Revised, updated and additional results were shown and analysed, which provide more evidence to show that the new proposed ISAAS model has a better accuracy than the Saastamoinen in Russia. [ABSTRACT FROM AUTHOR]

The electromagnetic signals of the Global Navigation Satellite Systems (GNSS) satellites suffer delays while propagating through the troposphere. The tropospheric delay is a significant systematic error of GNSS positioning. For safety-of-life applications of positioning many systematic error effects are either mitigated or eliminated in the positioning solution. Space based augmentation systems provide corrections for the orbital and satellite clock error, the ionospheric effects, etc. Moreover advanced GNSS provide dual frequency code observations for civilian users to eliminate the ionospheric delays caused by the electron content of the upper atmosphere. Nevertheless tropospheric delays are still taken into account using empirical models. For safety-of-life applications besides the accuracy of the positioning, the integrity of the positioning service is an important factor, too. The integrity information includes the maximal positioning error at an extremely rare probability level, called protection level to ensure highly reliable position solution in the aviation. The Radio Technical Commission for Aeronautics Minimum Operational Performance Standard (RTCA MOPS) recommends 0.12 m as the maximum zenith tropospheric error in terms of standard deviation. Previous studies show that this recommendation seems to be too conservative leading to a lower service availability. Therefore a more realistic integrity model has to be derived for the estimation of maximal residual tropospheric delay error. In the recent years many advanced empirical tropospheric delay models have been formulated compared to the one recommended by the RTCA. Recently new integrity models have been derived for estimating the maximum residual tropospheric delay error using numerical weather models under real extreme weather. The aim of this paper is to study the reliability of these models conditions. In order to achieve this, high-resolution numerical weather models were ray-traced using an improved ray-tracing algorithm to evaluate the slant and zenith tropospheric delays with the geographical resolution of 0.1° × 0.1°. [ABSTRACT FROM AUTHOR]

Abstract: To investigate future variations in high‐impact weather events, numerous samples are required. For the detailed assessment in a specific region, a high spatial resolution is also required. A simple ensemble simulation technique is proposed in this paper. In the proposed technique, new ensemble members were generated from one basic state vector and two perturbation vectors, which were obtained by lagged average forecasting simulations. Sensitivity experiments with different numbers of ensemble members, different simulation lengths, and different perturbation magnitudes were performed. Experimental application to a global warming study was also implemented for a typhoon event. Ensemble‐mean results and ensemble spreads of total precipitation, atmospheric conditions showed similar characteristics across the sensitivity experiments. The frequencies of the maximum total and hourly precipitation also showed similar distributions. These results indicate the robustness of the proposed technique. On the other hand, considerable ensemble spread was found in each ensemble experiment. In addition, the results of the application to a global warming study showed possible variations in the future. These results indicate that the proposed technique is useful for investigating various meteorological phenomena and the impacts of global warming. The results of the ensemble simulations also enable the stochastic evaluation of differences in high‐impact weather events. In addition, the impacts of a spectral nudging technique were also examined. The tracks of a typhoon were quite different between cases with and without spectral nudging; however, the ranges of the tracks among ensemble members were comparable. It indicates that spectral nudging does not necessarily suppress ensemble spread. [ABSTRACT FROM AUTHOR]

The multiple global navigation satellite systems (multi-GNSS) bring great opportunity for the real-time retrieval of high-quality zenith tropospheric delay (ZTD), which is a critical quality for atmospheric science and geodetic applications. In this contribution, a multi-GNSS precise point positioning (PPP) ambiguity resolution (AR) analysis approach is developed for real-time tropospheric delay retrieval. To validate the proposed multi-GNSS ZTD estimates, we collected and processed data from 30 Multi-GNSS Experiment (MGEX) stations; the resulting real-time tropospheric products are evaluated by using standard post-processed troposphere products and European Centre for Medium-Range Weather Forecasts analysis (ECMWF) data. An accuracy of 4.5 mm and 7.1 mm relative to the Center for Orbit Determination in Europe (CODE) and U.S. Naval Observatory (USNO) products is achievable for real-time tropospheric delays from multi-GNSS PPP ambiguity resolution after an initialization process of approximately 5 min. Compared to Global Positioning System (GPS) results, the accuracy of retrieved zenith tropospheric delay from multi-GNSS PPP-AR is improved by 16.7% and 31.7% with respect to USNO and CODE final products. The GNSS-derived ZTD time-series exhibits a great agreement with the ECMWF data for a long period of 30 days. The average root mean square (RMS) of the real-time zenith tropospheric delay retrieved from multi-GNSS PPP-AR is 12.5 mm with respect to ECMWF data while the accuracy of GPS-only results is 13.3 mm. Significant improvement is also achieved in terms of the initialization time of the multi-GNSS tropospheric delays, with an improvement of 50.7% compared to GPS-only fixed solutions. All these improvements demonstrate the promising prospects of the multi-GNSS PPP-AR method for time-critical meteorological applications. [ABSTRACT FROM AUTHOR]

Haiyang-2A (HY-2A) has been working in-flight for over seven years, and the accuracy of HY-2A calibration microwave radiometer (CMR) data is extremely important for the wet troposphere delay correction (WTC) in sea surface height (SSH) determination. We present a comprehensive evaluation of the HY-2A CMR observation using the numerical weather model (NWM) for all the data available period from October 2011 to February 2018, including the WTC and the precipitable water vapor (PWV). The ERA(ECMWF Re-Analysis)-Interim products from European Centre for Medium-Range Weather Forecasts (ECMWF) are used for the validation of HY-2A WTC and PWV products. In general, a global agreement of root-mean-square (RMS) of 2.3 cm in WTC and 3.6 mm in PWV are demonstrated between HY-2A observation and ERA-Interim products. Systematic biases are revealed where before 2014 there was a positive WTC/PWV bias and after that, a negative one. Spatially, HY-2A CMR products show a larger bias in polar regions compared with mid-latitude regions and tropical regions and agree better in the Antarctic than in the Arctic with NWM. Moreover, HY-2A CMR products have larger biases in the coastal area, which are all caused by the brightness temperature (TB) contamination from land or sea ice. Temporally, the WTC/PWV biases increase from October 2011 to March 2014 with a systematic bias over 1 cm in WTC and 2 mm in PWV, and the maximum RMS values of 4.62 cm in WTC and 7.61 mm in PWV occur in August 2013, which is because of the unsuitable retrieval coefficients and systematic TB measurements biases from 37 GHz band. After April 2014, the TB bias is corrected, HY-2A CMR products agree very well with NWM from April 2014 to May 2017 with the average RMS of 1.68 cm in WTC and 2.65 mm in PWV. However, since June 2017, TB measurements from the 18.7 GHz band become unstable, which led to the huge differences between HY-2A CMR products and the NWM with an average RMS of 2.62 cm in WTC and 4.33 mm in PWV. HY-2A CMR shows high accuracy when three bands work normally and further calibration for HY-2A CMR is in urgent need. Furtherly, 137 global coastal radiosonde stations were used to validate HY-2A CMR. The validation based on radiosonde data shows the same variation trend in time of HY-2A CMR compared to the results from ECMWF, which verifies the results from ECMWF.

The correction of tropospheric influences via so-called path delays is critical for the analysis of observations from space geodetic techniques like the very long baseline interferometry (VLBI). In standard VLBI analysis, the a priori slant path delays are determined using the concept of zenith delays, mapping functions and gradients. The a priori use of ray-traced delays, i.e., tropospheric slant path delays determined with the technique of ray-tracing through the meteorological data of numerical weather models (NWM), serves as an alternative way of correcting the influences of the troposphere on the VLBI observations within the analysis. In the presented research, the application of ray-traced delays to the VLBI analysis of sessions in a time span of 16.5 years is investigated. Ray-traced delays have been determined with program RADIATE (see Hofmeister in Ph.D. thesis, Department of Geodesy and Geophysics, Faculty of Mathematics and Geoinformation, Technische Universität Wien. , 2016) utilizing meteorological data provided by NWM of the European Centre for Medium-Range Weather Forecasts (ECMWF). In comparison with a standard VLBI analysis, which includes the tropospheric gradient estimation, the application of the ray-traced delays to an analysis, which uses the same parameterization except for the a priori slant path delay handling and the used wet mapping factors for the zenith wet delay (ZWD) estimation, improves the baseline length repeatability (BLR) at 55.9% of the baselines at sub-mm level. If no tropospheric gradients are estimated within the compared analyses, 90.6% of all baselines benefit from the application of the ray-traced delays, which leads to an average improvement of the BLR of 1 mm. The effects of the ray-traced delays on the terrestrial reference frame are also investigated. A separate assessment of the RADIATE ray-traced delays is carried out by comparison to the ray-traced delays from the National Aeronautics and Space Administration Goddard Space Flight Center (NASA GSFC) (Eriksson and MacMillan in , 2016) with respect to the analysis performances in terms of BLR results. If tropospheric gradient estimation is included in the analysis, 51.3% of the baselines benefit from the RADIATE ray-traced delays at sub-mm difference level. If no tropospheric gradients are estimated within the analysis, the RADIATE ray-traced delays deliver a better BLR at 63% of the baselines compared to the NASA GSFC ray-traced delays. [ABSTRACT FROM AUTHOR]

An interesting subject in the field of GPS technology is estimating variation of precipitable water vapor (PWV). This estimation can be used as a data source to assess and monitor rapid changes in meteorological conditions. So far, numerous GPS stations are distributed across the world and the number of GPS networks is increasing. Despite these developments, a challenging aspect of estimating PWV through GPS networks is the need of tropospheric parameters such as temperature, pressure, and relative humidity (Liu et al., 2015). To estimate the tropospheric parameters, global pressure temperature (GPT) model developed by Boehm et al. (2007) is widely used in geodetic analysis for GPS observations. To improve the accuracy, Lagler et al. (2013) introduced GPT2 model by adding annual and semi-annual variation effects to GPT model. Furthermore, Boehm et al. (2015) proposed the GPT2 wet (GPT2w) model which uses water vapor pressure to improve the calculations. The global accuracy of GPT2 and GPT2w models has been evaluated by previous researches (Fund et al., 2011; Munekane and Boehm, 2010); however, investigations to assess the accuracy of global tropospheric models in tropical regions such as Southeast Asia is not sufficient. This study tests and examines the accuracy of GPT2w as one of the most recent versions of tropospheric models (Boehm et al., 2015). We developed a new regional model called Malaysian Pressure Temperature (MPT) model, and compared this model with GPT2w model. The compared results at one international GNSS service (IGS) station located in the south of Peninsula Malaysia shows that MPT model has a better performance than GPT2w model to produce PWV during monsoon season. According to the results, MPT has improved the accuracy of estimated pressure and temperature by 30% and 10%, respectively, in comparison with GPT2w model. These results indicate that MPT model can be a good alternative tool in the absence of meteorological sensors at GPS stations in Peninsula Malaysia. Therefore, for GPS-based studies, we recommend MPT model to be used as a complementary tool for the Malaysia Real-Time Kinematic Network to develop a real-time PWV monitoring system. [ABSTRACT FROM AUTHOR]

This study sheds new light on the variability of wind power across the Australian NEM (National Electricity Market) and in doing so gives an insight on the potential network configuration for a high RE (Renewable Electricity) future. We present idealised cost-minimised simulations for the NEM utilising onshore wind, large-scale solar, pumped hydro and OCGT (open cycle gas turbines) technologies. A model using gridded meteorological data from the regional ACCESS-R (Australian Community Climate and Earth-System Simulator) simulates wind and solar technology output along with generation from OCGT to meet demand in the NEM for the period 2010–2011. A cost for connecting each location to the nearest major load centre is introduced and a base scenario created from an initial connection cost of $1 M/km. A sensitivity study reveals that a cost of $8 M/km results in the contraction of all renewable resources to four major wind installations. Compared to the base scenario the four major wind locations share much of the variability in renewable energy output, demonstrating that the NEM region has four distinct wind regimes. Separated by 1,400 km these four wind installations provide an optimisation-based decorrelation length for the NEM. This information is particularly useful for long-term planners of large-scale energy infrastructure. [ABSTRACT FROM AUTHOR]

Precise global navigation satellite system (GNSS) positioning requires an accurate mapping function (MF) to model the tropospheric delay. To date, the most accurate MF is the Vienna mapping function 1 (VMF1). It utilizes data from a numerical weather model and therefore captures the short-term variability of the atmosphere. Still, the VMF1, or any other MF that is based on the VMF1 concept, is a parameterized mapping approach, and this means that it is tuned for specific elevation angles, station heights, and orbital altitudes. In this study, we analyze the systematic errors caused by such tuning on a global scale. We find that, in particular, the parameterization of the station height dependency is a major concern regarding the application in complex terrain or airborne applications. At this time, we do not provide an improved parameterized mapping approach to mitigate the systematic errors but instead we propose a (ultra-) rapid direct mapping approach, the so-called Potsdam mapping factors (PMFs). Since for any station-satellite link the ratio of the tropospheric delay in the slant and zenith direction is computed directly, the PMFs effectively eliminate the systematic errors. [ABSTRACT FROM AUTHOR]

Atmospheric precipitable water vapor is an important element of the meteorological and meteorological disaster forecast. Using the ECMWF data for regional meteorological research is one of the current research focuses. For the problem caused by the low spatial resolution of the ECMWF data in studying the small regional meteorologicala, we studied the ECMWF water vapor interpolation idea with the Stretched Boundary Layer, and we established the model and realized it. This model can be used to interpolate the ECMWF water vapor, surface pressure and surface temperature. We can obtain the results in any density based on region DEM resolution. The model is tested with the GPS water vapor observations and surface meteorological data from the GPS-IPW network of the U. S. The results show that the model can improve the accuracy greatly on the ECMWF water vapor, surface pressure and surface temperature compared to the raw ECMWF data. [ABSTRACT FROM AUTHOR]

Wind vector fields derived from synthetic aperture radar (SAR) sensors show variations at smaller scales than most other globally available surface wind sources. However, few studies have been devoted to the investigation of the accuracy of SAR-derived wind fields at different scales and how they compare with other wind data. In order to investigate these issues, an algorithm for the retrieval of SAR-derived wind vectors has been developed, and a quality assessment between the retrievals and in situ, scatterometer, and numerical weather model (NWM) wind data has been performed. The implemented wind retrieval algorithm detects streak features in the SAR image to estimate wind directions and inverts wind speeds using CMOD-IFR2, CMOD5, or CMOD5.N geophysical model functions. In addition, a regularization method for filtering outliers in the wind direction retrievals is used. Retrievals compared with in situ data indicated better performance at offshore locations for wind speed inversions with CMOD5.N. The bias and standard deviation for offshore regularized wind directions and CMOD5.N speeds are 9° and 25° and -0.1 and 1.4 m/s, respectively. The comparison with the scatterometer and NWM wind data has been performed for retrievals at 5-, 10-, and 20-km resolution. The results indicate a better agreement of the coarser retrievals with the reference data. Nevertheless, mapping of smaller scale features requires wind directions from the SAR image itself. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel Bayesian procedure to update the probability distribution for a set of possible atmospheric states, once ground measures of temperature, pressure, humidity, and tropospheric delay of Global Navigation Satellite System (GNSS) signals are made. It is based on a representative dataset of matching pairs of reanalysis atmospheric states and ground measures. By applying the basic rules of probability theory and logic inference, a computable expression for the conditional probability of the states given the measures is found. This allows us to select the most plausible atmospheric conditions, consistent with ground observations. Compared with more conventional techniques, the proposed approach has the advantage of always giving a result, even if not all the measures are available. Moreover, it provides the probability distributions of the retrieved quantities, which collapse to the corresponding prior distributions in the worst case of no significant measures. In any case, the final uncertainties are fully quantified, as needed for many meteorological applications, including data assimilation and ensemble forecasts for a numerical weather model. In addition to the theoretical details, a practical example of operational application, using a ten-year dataset on a Mediterranean test site, is also presented. The most probable retrieved atmospheric profiles of water vapor and temperature, as well as the corresponding values of precipitable water, are compared with balloon measurements on such a test site, showing good agreement and a significant improvement when the GNSS delay measure is added. In particular, the precipitable water retrieval turns out at least as accurate as that obtained with conventional approaches. [ABSTRACT FROM PUBLISHER]

The aim of this paper is to review of six recent symmetric mapping functions. The mapping function can be largely used for GPS meteorological measurements, InSAR atmospheric corrections and precise measurements of very long baseline interferometry (VLBI). These spacebased techniques use radio signal that propagate through the Earth's atmosphere. The electrically-neutral region, predominantly the troposphere, affects the speed and direction of travel of radio waves leading to existence of excess path. The mapping function models the elevation angle dependence of the delay. Within the past decade, significant improvements have been achieved in order to use of Numerical Weather Models (NWM) for geodetic positioning. Ray-tracing algorithms have been performed through refractivity shells retrieved from NWMs in order to relate zenith delays to slant delays. Therefore, there seems to be a real need for deep review of recent developments in the mapping function domain. This paper proposes a comprehensive review of the symmetric mapping functions state of the art, their spatio-temporal variations and used NWM and generic models. Niell Mapping Function (NMF), Vienna Mapping Function (VMF1), University of New Brunswick-VMF1 (UNB-VMF1) mapping functions, Global Mapping Function (GMF) and Global Pressure and Temperature (GPT2)/GMF are reviewed in this paper. [ABSTRACT FROM AUTHOR]

The EUMETNET EIG GNSS Water Vapour Programme (E-GVAP) is responsible for the coordination of near real time GPS Zenith Total Delay (ZTD) production in Europe and for aiding the development of ZTD assimilation into Numerical Weather Prediction (NWP) models. Since 2000, the Geodetic Observatory Pecný (GOP) has been routinely estimating regional ZTDs in near real time. In 2010, GOP developed a modified processing system in order to provide the first optimal and robust ZTD solution with a global scope and hourly upgrade, fulfilling the requirements for assimilation into operational NWP models. Since July 2010, the GOP global tropospheric product has consisted of about 90 sites and has contributed routinely in a testing mode into the E-GVAP database. Global near real time ZTDs generated over ten months have been evaluated with respect to IGS and EUREF routine post-processed ZTD products, ZTDs integrated from radiosonde profiles, and ZTDs calculated from the Met Office global NWP model. Comparison with the GNSS post-processed solutions gives standard deviations of 3-6 mm in ZTD and biases of 1-2 mm, which is comparable to GOP regional near real time solution, however, for some isolated or low data quality stations up to 20 % quality decrease can be found. Comparison with NWP shows a latitudinal trend in the standard deviation with values as low as 4 mm at high latitudes, increasing to almost 20 mm in the tropics, and a lack of variability in the model background ZTD in the tropics. The evaluation with global radiosondes gives ZTD standard deviation of 5-16 mm, which is comparable with previous studies in European scope. Since the 10-month comparison gave satisfactory results, GOP was asked by UK Met Office to disseminate the global product to the end users via the Global Telecommunications System. Since 10 October 2011, the GOP global ZTD product configuration has been extended to about 164 global stations and still processed within 10 min. However, in GOP routine contribution to E-GVAP, about 124 stations are available in general due to hourly data latency above 30 min or data gaps. [ABSTRACT FROM AUTHOR]

Forecast-based flow prediction in drainage systems can be used to implement real-time control of drainage systems. This study compares two different types of rainfall forecast - a radar rainfall extrapolation-based nowcast model and a numerical weather prediction model. The models are applied as input to an urban runoff model predicting the inlet flow to a waste water treatment plant. The modelled flows are auto-calibrated against real-time flow observations in order to certify the best possible forecast. Results show that it is possible to forecast flows with a lead time of 24 h. The best performance of the system is found using the radar nowcast for the short lead times and the weather model for larger lead times. [ABSTRACT FROM AUTHOR]

Interferometric synthetic aperture radar phase data include not only signals due to crustal movements, but also those associated with microwave propagation delay through the atmosphere. In particular, the effect of water vapor can generate apparent signals in the order of a few centimeters or more, and prevent us from detecting such geophysical signals as those due to secular crustal deformation. To examine if and to what extent numerical weather model (NWM) outputs are helpful to reduce the tropospheric delay signals at spatial scales of 5-50 km wavelengths, we compared three approaches of tropospheric signal reduction, using 54 interferograms in central Hokkaido, Japan. The first approach is the conventional topography-correlated delay correction that is based on the regional digital elevation model (DEM). The second approach is based on the Japan Meteorological Agency's operational meso-scale analysis model (MSM) data, where we compute tropospheric delays and subtract them from the interferogram. However, the MSM data are available at predefined epochs and their spatial resolution is about 10 km; therefore, we need to interpolate both temporally and spatially to match with interferograms. Expecting to obtain a more physically plausible reduction of the tropospheric effects, we ran a 1-km mesh high-resolution numerical weather model WRF (Weather Research and Forecasting model) by ourselves, using the MSM data as the initial and boundary conditions. The third approach is similar to the second approach, except that we make use of the WRF-based tropospheric data. Results show that if the topography-correlated phases are significant, both the conventional DEM-based approach and the MSM-based approach reveal comparable performances. However, when the topography-correlated phases are insignificant, none of the approaches can efficiently reduce the tropospheric phases. Although it could reduce the tropospheric signals in a local area, in none of the case studies did the WRF model produce the 'best' performance. Whereas the global atmospheric model outputs are shown to be effective in reducing long-wavelength tropospheric signals, we consider that further improvements are needed for the initial and boundary condition data for high-resolution NWM, so that the NWM-based approach will become more reliable even in the case of a non-stratified troposphere. [ABSTRACT FROM AUTHOR]

This work experiments the potentialities of COSMO/SkyMed (CSK) data in providing interferometric Digital Elevation Model (DEM). We processed a stack of CSK data for measuring with meter accuracy the ground elevation on the available coherent targets, and used these values to check the accuracy of DEMs derived from 5 tandem-like CSK pairs. In order to suppress the atmospheric signal we experimented a classical spatial filtering of the differential phase as well as the use of numerical weather prediction (NWP) model RAMS. Tandem-like pairs with normal baselines higher than 300 m allows to derive DEMs fulfilling the HRTI Level 3 specifications on the relative vertical accuracy, while the use of NWP models still seems unfeasible especially for X-band. [ABSTRACT FROM PUBLISHER]

Numerical weather models offer the possibility to compute corrections for a variety of space geodetic applications, including remote sensing techniques like interferometric SAR. Due to the computational complexity, exact ray-tracing is avoided in many cases and mapping approaches are applied to transform vertically integrated delay corrections into slant direction. Such an approach works well as long as lateral atmospheric gradients are small enough to be neglected. But since such an approximation holds only for very rare cases it is investigated how horizontal gradients of different atmospheric constituents can evoke errors caused by the mapping strategy. Moreover, it is discussed how sudden changes of wet refractivity can easily lead to millimeter order biases when simplified methods are applied instead of ray-tracing. By an example, based on real InSAR data, the differences of the various troposphere correction schemes are evaluated and it is shown how the interpretation of the geophysical signals can be affected. In addition, it is studied to which extend troposphere noise can be reduced by applying the exact ray-tracing solution. [ABSTRACT FROM AUTHOR]

In GNSS analysis, the tropospheric delay is parameterized by applying mapping functions (MFs), zenith delays, and tropospheric gradients. Thereby, the wet and hydrostatic MF are derived under the assumption of a spherically layered atmosphere. The coefficients of the closed-form expression are computed utilizing a climatology or numerical weather model (NWM) data. In this study, we analyze the impact of tropospheric mismodelling on estimated parameters in precise point positioning (PPP). To do so, we mimic PPP in an artificial environment, i.e., we make use of a linearized observation equation, where the observed minus modelled term equals ray-traced tropospheric delays from a high-resolution NWM. The estimated parameters (station coordinates, clocks, zenith delays, and tropospheric gradients) are then compared with the known values. The simulation study utilized a cut-off elevation angle of 3° and the standard downweighting of low elevation angle observations. The results are representative of a station located in central Europe and the warm season. In essence, when climatology is utilized in GNSS analysis, the root mean square error (RMSE) of the estimated zenith delay and station up-component equal about 2.9 mm and 5.7 mm, respectively. The error of the GNSS estimates can be reduced significantly if the correct zenith hydrostatic delay and the correct hydrostatic MF are utilized in the GNSS analysis. In this case, the RMSE of the estimated zenith delay and station up-component is reduced to about 2.0 mm and 2.9 mm, respectively. The simulation study revealed that the choice of wet MF, when calculated under the assumption of a spherically layered troposphere, does not matter too much. In essence, when the 'correct' wet MF is utilized in the GNSS analysis, the RMSE of the estimated zenith delay and station up-component remain at about 1.8 mm and 2.4 mm, respectively. Finally, as a by-product of the simulation study, we developed a modified wet MF, which is no longer based on the assumption of a spherically layered atmosphere. We show that with this modified wet MF in the GNSS analysis, the RMSE of the estimated zenith delay and station up-component can be reduced to about 0.5 mm and 1.0 mm, respectively. In practice, its success depends on the ability of current (future) NWM to predict the fourth coefficient of the developed closed-form expression. We provide some evidence that current NWMs are able to do so. [ABSTRACT FROM AUTHOR]

Hydrological ensemble prediction systems (HEPSs) can provide decision makers with early warning information, such as peak stage and peak time, with enough lead time to take the necessary measures to mitigate disasters. This study develops a HEPS that integrates meteorological, hydrological, storm surge, and global tidal models. It is established to understand information about the uncertainty of numerical weather predictions and then to provide probabilistic flood forecasts instead of commonly adopted deterministic forecasts. The accuracy of flood forecasting is increased. However, the spatiotemporal uncertainty associated with these numerical models in the HEPS and the difficulty in interpreting the model results hinder effective decision-making during emergency response situations. As a result, the efficiency of decision-making is not always increased. Thus, this study also presents a visualization method to interpret the ensemble results to enhance the understanding of probabilistic runoff forecasts for operational purposes. A small watershed with area of 100 km2 and four historical typhoon events were selected to evaluate the performance of the method. The results showed that the proposed HEPS along with the visualization approach improved the intelligibility of forecasts of the peak stages and peak times compared to that of approaches previously described in the literature. The capture rate is greater than 50%, which is considered practical for decision makers. The proposed HEPS with the visualization method has potential for both decreasing the uncertainty of numerical rainfall forecasts and improving the efficiency of decision-making for flood forecasts. [ABSTRACT FROM AUTHOR]