Real-Time Water Vapor Maps from a GPS Surface Network: Construction, Validation, and Applications

2009 ◽  
Vol 48 (7) ◽  
pp. 1302-1316 ◽  
Author(s):  
Siebren de Haan ◽  
Iwan Holleman ◽  
Albert A. M. Holtslag
Keyword(s):  
Water Vapor ◽  
Standard Deviation ◽  
Real Time ◽  
Weather Prediction ◽  
Surface Wind ◽  
Step Method ◽  
Background Error ◽  
Surface Network ◽  
Nwp Model ◽  
Additional Value

Abstract In this paper the construction of real-time integrated water vapor (IWV) maps from a surface network of global positioning system (GPS) receivers is presented. The IWV maps are constructed using a two-dimensional variational technique with a persistence background that is 15 min old. The background error covariances are determined using a novel two-step method, which is based on the Hollingsworth–Lonnberg method. The quality of these maps is assessed by comparison with radiosonde observations and IWV maps from a numerical weather prediction (NWP) model. The analyzed GPS IWV maps have no bias against radiosonde observations and a small bias against NWP analysis and forecasts up to 9 h. The standard deviation with radiosonde observations is around 2 kg m−2, and the standard deviation with NWP increases with increasing forecast length (from 2 kg m−2 for the NWP analysis to 4 kg m−2 for a forecast length of 48 h). To illustrate the additional value of these real-time products for nowcasting, three thunderstorm cases are discussed. The constructed GPS IWV maps are combined with data from the weather radar, a lightning detection network, and surface wind observations. All cases show that the location of developing thunderstorms can be identified 2 h prior to initiation in the convergence of moist air.

scholarly journals Comparative Studies for the Assessment of the Quality of Near-Real-Time GPS-Derived Atmospheric Parameters

2008 ◽  
Vol 25 (5) ◽  
pp. 701-714 ◽  
Author(s):  
R. Pacione ◽  
F. Vespe
Keyword(s):  
Water Vapor ◽  
Standard Deviation ◽  
Real Time ◽  
Weather Prediction ◽  
Neutral Atmosphere ◽  
Atmospheric Parameters ◽  
Total Delay ◽  
Zenith Total Delay ◽  
Wide Range

Abstract Accurate and frequent sampling of atmospheric parameters, such as water vapor, is important for enabling reliable weather forecasts and global climate studies over a wide range of spatial and temporal scales. Recent developments in global positioning system data processing have allowed the estimation of zenith total delay (ZTD), the delay of the neutral atmosphere, with a high degree of accuracy using continuously operating GPS networks. From this delay integrated water vapor can be derived by means of additional meteorological information, in particular observed pressure or numerical weather prediction model pressure. Comparisons with other independent techniques must be performed to evaluate the quality of atmospheric parameters directly estimated or retrieved from the GPS system. In this work the accuracy of GPS atmospheric parameter, namely, zenith total delay, delivered in near–real time from a European ground-based network of permanent GPS receivers has been assessed. It is compared to other GPS solutions, radiosonde profiles, and High-Resolution Limited-Area Model (HIRLAM)-derived ZTD. Intercomparisons between results from different GPS analysis centers in the framework of the Targeting Optimal Use of GPS Humidity Measurements in Meteorology (TOUGH) project show a mean ZTD station bias at the level of ±6 mm with a related standard deviation of about 7–8 mm. In the comparison with radiosondes, an overall ZTD bias of about 7 mm with a standard deviation of 9 mm is detected. Finally, the comparison of ZTD near–real time against the HIRLAM models has an average bias of about −4.8 mm and a standard deviation of 11.5 mm.

scholarly journals HGPT2: An ERA5-Based Global Model to Estimate Relative Humidity

2021 ◽  
Vol 13 (11) ◽  
pp. 2179
Author(s):  
Pedro Mateus ◽  
Virgílio B. Mendes ◽  
Sandra M. Plecha
Keyword(s):  
Water Vapor ◽  
Relative Humidity ◽  
Real Time ◽  
Prediction Models ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Global Navigation Satellite Systems ◽  
International Gnss Service ◽  
Weighted Mean Temperature

The neutral atmospheric delay is one of the major error sources in Space Geodesy techniques such as Global Navigation Satellite Systems (GNSS), and its modeling for high accuracy applications can be challenging. Improving the modeling of the atmospheric delays (hydrostatic and non-hydrostatic) also leads to a more accurate and precise precipitable water vapor estimation (PWV), mostly in real-time applications, where models play an important role, since numerical weather prediction models cannot be used for real-time processing or forecasting. This study developed an improved version of the Hourly Global Pressure and Temperature (HGPT) model, the HGPT2. It is based on 20 years of ERA5 reanalysis data at full spatial (0.25° × 0.25°) and temporal resolution (1-h). Apart from surface air temperature, surface pressure, zenith hydrostatic delay, and weighted mean temperature, the updated model also provides information regarding the relative humidity, zenith non-hydrostatic delay, and precipitable water vapor. The HGPT2 is based on the time-segmentation concept and uses the annual, semi-annual, and quarterly periodicities to calculate the relative humidity anywhere on the Earth’s surface. Data from 282 moisture sensors located close to GNSS stations during 1 year (2020) were used to assess the model coefficients. The HGPT2 meteorological parameters were used to process 35 GNSS sites belonging to the International GNSS Service (IGS) using the GAMIT/GLOBK software package. Results show a decreased root-mean-square error (RMSE) and bias values relative to the most used zenith delay models, with a significant impact on the height component. The HGPT2 was developed to be applied in the most diverse areas that can significantly benefit from an ERA5 full-resolution model.

Real-time and near real-time ZTD from a local network of low-cost dual-frequency GNSS receivers.

2021 ◽  
Author(s):  
Tomasz Hadas ◽  
Grzegorz Marut ◽  
Jan Kapłon ◽  
Witold Rohm
Keyword(s):  
Water Vapor ◽  
Real Time ◽  
Low Cost ◽  
Weather Prediction ◽  
Local Network ◽  
System Component ◽  
Dual Frequency ◽  
Lower Accuracy ◽  
Gnss Receivers ◽  
The Impact

<p>The dynamics of water vapor distribution in the troposphere, measured with Global Navigation Satellite Systems (GNSS), is a subject of weather research and climate studies. With GNSS, remote sensing of the troposphere in Europe is performed continuously and operationally under the E-GVAP (http://egvap.dmi.dk/) program with more than 2000 permanent stations. These data are one of the assimilation system component of mesoscale weather prediction models (10 km scale) for many nations across Europe. However, advancing precise local forecasts for severe weather requires high resolution models and observing system.   Further densification of the tracking network, e.g. in urban or mountain areas, will be costly when considering geodetic-grade equipment. However, the rapid development of GNSS-based applications results in a dynamic release of mass-market GNSS receivers. It has been demonstrated that post-processing of GPS-data from a dual-frequency low-cost receiver allows retrieving ZTD with high accuracy. Although low-cost receivers are a promising solution to the problem of densifying GNSS networks for water vapor monitoring, there are still some technological limitations and they require further development and calibration.</p><p>We have developed a low-cost GNSS station, dedicated to real-time GNSS meteorology, which provides GPS, GLONASS and Galileo dual-frequency observations either in RINEX v3.04 format or via RTCM v3.3 stream, with either Ethernet or GSM data transmission. The first two units are deployed in a close vicinity of permanent station WROC, which belongs to the International GNSS Service (IGS) network. Therefore, we compare results from real-time and near real-time processing of GNSS observations from a low-cost unit with IGS Final products. We also investigate the impact of replacing a standard patch antenna with an inexpensive survey-grade antenna. Finally, we deploy a local network of low-cost receivers in and around the city of Wroclaw, Poland, in order to analyze the dynamics of troposphere delay at a very high spatial resolution.</p><p>As a measure of accuracy, we use the standard deviation of ZTD differences between estimated ZTD and IGS Final product. For the near real-time mode, that accuracy is 5 mm and 6 mm, for single- (L1) and dual-frequency (L1/L5,E5b) solution, respectively. Lower accuracy of the dual-frequency relative solution we justify by the missing antenna phase center correction model for L5 and E5b frequencies. With the real-time Precise Point Positioning technique, we estimate ZTD with the accuracy of 7.5 – 8.6 mm. After antenna replacement, the accuracy is improved almost by a factor of 2 (to 4.1 mm), which is close to the 3.1 mm accuracy which we obtain in real-time using data from the WROC station.</p>

scholarly journals Near Real Time GPS Water Vapor Monitoring for Numerical Weather Prediction in Germany

2004 ◽  
Vol 82 (1B) ◽  
pp. 361-370 ◽  
Author(s):  
Gerd GENDT ◽  
Galina DICK ◽  
Christoph REIGBER ◽  
Maria TOMASSINI ◽  
Yanxiong LIU ◽  
...  
Keyword(s):  
Water Vapor ◽  
Real Time ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Numerical Weather

scholarly journals High-resolution numerical modeling of mesoscale island wakes and sensitivity to static topographic relief data

2015 ◽  
Vol 8 (8) ◽  
pp. 2645-2653 ◽  
Author(s):  
C. G. Nunalee ◽  
Á. Horváth ◽  
S. Basu
Keyword(s):  
High Resolution ◽  
Vortex Shedding ◽  
Weather Prediction ◽  
Surface Wind ◽  
Wrf Model ◽  
Data Sets ◽  
Data Set ◽  
Near Surface ◽  
Nwp Model ◽  
Height Data

Abstract. Recent decades have witnessed a drastic increase in the fidelity of numerical weather prediction (NWP) modeling. Currently, both research-grade and operational NWP models regularly perform simulations with horizontal grid spacings as fine as 1 km. This migration towards higher resolution potentially improves NWP model solutions by increasing the resolvability of mesoscale processes and reducing dependency on empirical physics parameterizations. However, at the same time, the accuracy of high-resolution simulations, particularly in the atmospheric boundary layer (ABL), is also sensitive to orographic forcing which can have significant variability on the same spatial scale as, or smaller than, NWP model grids. Despite this sensitivity, many high-resolution atmospheric simulations do not consider uncertainty with respect to selection of static terrain height data set. In this paper, we use the Weather Research and Forecasting (WRF) model to simulate realistic cases of lower tropospheric flow over and downstream of mountainous islands using the default global 30 s United States Geographic Survey terrain height data set (GTOPO30), the Shuttle Radar Topography Mission (SRTM), and the Global Multi-resolution Terrain Elevation Data set (GMTED2010) terrain height data sets. While the differences between the SRTM-based and GMTED2010-based simulations are extremely small, the GTOPO30-based simulations differ significantly. Our results demonstrate cases where the differences between the source terrain data sets are significant enough to produce entirely different orographic wake mechanics, such as vortex shedding vs. no vortex shedding. These results are also compared to MODIS visible satellite imagery and ASCAT near-surface wind retrievals. Collectively, these results highlight the importance of utilizing accurate static orographic boundary conditions when running high-resolution mesoscale models.

scholarly journals Asymmetricity of ground-based GPS slant delay data

2007 ◽  
Vol 7 (12) ◽  
pp. 3143-3151 ◽  
Author(s):  
R. Eresmaa ◽  
H. Järvinen ◽  
S. Niemelä ◽  
K. Salonen
Keyword(s):  
Standard Deviation ◽  
Global Positioning System ◽  
Weather Prediction ◽  
Small Scale ◽  
Tropospheric Delay ◽  
Observation Error ◽  
Positioning System ◽  
Global Positioning ◽  
Nwp Model ◽  
Delay Component

Abstract. The ground-based measurements of the Global Positioning System (GPS) allow estimation of the tropospheric delay along the slanted signal paths through the atmosphere. The meteorological exploitation of such slant delay (SD) observations relies on the hypothesis of azimuthal asymmetry of the information content. This article addresses the validity of the hypothesis. A new concept of asymmetricity is introduced for studying the SD observations and their model counterparts. The asymmetricity is defined as the ratio of the absolute asymmetric delay component to total SD. The model counterparts are determined from 3-h forecasts of a numerical weather prediction (NWP) model, run with four different horizontal resolutions. The SD observations are compared with their model counterparts with emphasis on cases of high asymmetricity in order to see whether the observed asymmetry is a real atmospheric signature. The asymmetricity is found to be of the order of a few parts per thousand. Thus, the asymmetric delay component barely exceeds the assumed standard deviation of the SD observation error. However, the observed asymmetric delay components show a statistically significant meteorological signal. Benefit of the asymmetric SD observations is therefore expected to be taken in future, when NWP systems will explicitly represent the small-scale atmospheric features revealed by the SD observations.

scholarly journals Assessment of the Hyperspectral Infrared Atmospheric Sounder (HIRAS)

2019 ◽  
Vol 11 (24) ◽  
pp. 2950 ◽  
Author(s):  
Fabien Carminati ◽  
Xianjun Xiao ◽  
Qifeng Lu ◽  
Nigel Atkinson ◽  
James Hocking
Keyword(s):  
Standard Deviation ◽  
Spectral Resolution ◽  
Weather Prediction ◽  
Field Of View ◽  
Double Difference ◽  
Fourier Transform Spectrometer ◽  
Spectral Bands ◽  
Data Quality Assessment ◽  
Nwp Model ◽  
Good Agreement

The hyperspectral infrared atmospheric sounder (HIRAS), the first Chinese hyperspectral infrared instrument, was launched in 2017 on board the fourth polar orbiter of the Feng Yun 3 series, FY-3D. The instrument is a Fourier transform spectrometer with 2275 channels covering three spectral bands (650–1136, 1210–1750, and 2155–2550 cm−1) with 0.625 cm−1 spectral resolution. The first data quality assessment of HIRAS observations at full and normal spectral resolutions is presented. Comparisons with short-range forecasts from the Met Office numerical weather prediction (NWP) global system have revealed biases (standard deviation) generally less than 2.6 K (2 K) in the spectral regions mostly unaffected by trace gases where the confidence in the NWP model is largest. Of particular concern, HIRAS detector 3 seems to suffer from sunlight contamination of its calibration towards the end of the descending node. This, together with an obstruction of the detector field of view by an element of the platform, results in accentuated bias and noise in the observations from this detector. At normal spectral resolution, a background departure double difference analysis has been conducted between HIRAS and the NOAA-20 crosstrack infrared sounder (CrIS). The results show that HIRAS and CrIS are in good agreement with a mean difference across the three bands of −0.05 K (±0.26 K at 1σ) and 75.2% of the channels within CrIS radiometric uncertainty, noting though that HIRAS is noisier than CrIS with, on average, a standard deviation 0.34 K larger.

scholarly journals A methodology for probabilistic real-time forecasting – an urban case study

2012 ◽  
Vol 15 (3) ◽  
pp. 751-762 ◽  
Author(s):  
Jeanne-Rose René ◽  
Henrik Madsen ◽  
Ole Mark
Keyword(s):  
Real Time ◽  
Probability Distributions ◽  
Methodological Approach ◽  
Weather Prediction ◽  
Probabilistic Approach ◽  
Latin Hypercube Sampling ◽  
Social Consequences ◽  
Complex Nature ◽  
Modelling Uncertainty ◽  
Nwp Model

The phenomenon of urban flooding due to rainfall exceeding the design capacity of drainage systems is a global problem and can have significant economic and social consequences. The complex nature of quantitative precipitation forecasts (QPFs) from numerical weather prediction (NWP) models has facilitated a need to model and manage uncertainty. This paper presents a probabilistic approach for modelling uncertainty from single-valued QPFs at different forecast lead times. The uncertainty models in the form of probability distributions of rainfall forecasts combined with a sewer model is an important advancement in real-time forecasting at the urban scale. The methodological approach utilized in this paper involves a retrospective comparison between historical forecasted rainfall from a NWP model and observed rainfall from rain gauges from which conditional probability distributions of rainfall forecasts are derived. Two different sampling methods, respectively, a direct rainfall quantile approach and the Latin hypercube sampling-based method were used to determine the uncertainty in forecasted variables (water level, volume) for a test urban area, the city of Aarhus. The results show the potential for applying probabilistic rainfall forecasts and their subsequent use in urban drainage forecasting for estimation of prediction uncertainty.

scholarly journals An Innovative Calibration Method for the Inversion of Satellite Observations

2010 ◽  
Vol 49 (12) ◽  
pp. 2458-2473 ◽  
Author(s):  
Filipe Aires ◽  
Frédéric Bernardo ◽  
Héléne Brogniez ◽  
Catherine Prigent
Keyword(s):  
Water Vapor ◽  
Single Channel ◽  
Weather Prediction ◽  
Calibration Method ◽  
Calibration Procedure ◽  
Satellite Observations ◽  
Radiative Transfer Model ◽  
Sensor Calibration ◽  
Transfer Model ◽  
Nwp Model

Abstract Retrieval schemes often use two important components: 1) a radiative transfer model (RTM) inside the retrieval procedure or to construct the learning dataset for the training of the statistical retrieval algorithms and 2) a numerical weather prediction (NWP) model to provide a first guess or, again, to construct a learning dataset. This is particularly true in operational centers. As a consequence, any physical retrieval or similar method is limited by inaccuracies in the RTM and NWP models on which it is based. In this paper, a method for partially compensating for these errors as part of the sensor calibration is presented and evaluated. In general, RTM/NWP errors are minimized as best as possible prior to the training of the retrieval method, and then tolerated. The proposed method reduces these unknown and generally nonlinear residual errors by training a separate preprocessing neural network (NN) to produce calibrated radiances from real satellite data that approximate those radiances produced by the “flawed” NWP and RTM models. The final “compensated/flawed” retrieval assures better internal consistency of the retrieval procedure and then produces more accurate results. To the authors’ knowledge, this type of NN model has not been used yet for this purpose. The calibration approach is illustrated here on one particular application: the retrieval of atmospheric water vapor from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and the Humidity Sounder for Brazil (HSB) measurements for nonprecipitating scenes, over land and ocean. Before being inverted, the real observations are “projected” into the space of the RTM simulation space from which the retrieval is designed. Validation of results is performed with radiosonde measurements and NWP analysis departures. This study shows that the NN calibration of the AMSR-E/HSB observations improves water vapor inversion, over ocean and land, for both clear and cloudy situations. The NN calibration is efficient and very general, being applicable to a large variety of problems. The nonlinearity of the NN allows for the calibration procedure to be state dependent and adaptable to specific cases (e.g., the same correction will not be applied to medium-range measurement and to extreme conditions). Its multivariate nature allows for a full exploitation of the complex correlation structure among the instrument channels, making the calibration of each single channel more robust. The procedure would make it possible to project the satellite observations in a reference observational space defined by radiosonde measurements, RTM simulations, or other instrument observational space.

scholarly journals The AROME-WMED re-analyses of the first Special Observation Period of the Hydrological cycle in the Mediterranean experiment

2019 ◽  
Author(s):  
Nadia Fourrié ◽  
Mathieu Nuret ◽  
Pierre Brousseau ◽  
Olivier Caumont ◽  
Alexis Doerenbecher ◽  
...  
Keyword(s):  
Real Time ◽  
Hydrological Cycle ◽  
Weather Prediction ◽  
Forecast Errors ◽  
Error Statistics ◽  
Background Error ◽  
The Real ◽  
Western Mediterranean Basin ◽  
The Mediterranean ◽  
Observation Period

Abstract. To study key processes of the water cycle, two special observation periods (SOPs) of the Hydrological cycle in the Mediterranean experiment (HyMeX) took place during the autumn 2012 and winter 2013. The first SOP aimed to study high precipitation systems and flash-flooding in the Mediterranean area. The AROME-WMED (West-Mediterranean) model (Fourrié et al., 2015) is a dedicated version of the mesoscale Numerical Weather Prediction (NWP) AROME-France model 5 which covers the western Mediterranean basin providing the HyMeX operational centre with daily real-time analyses and forecasts. These products allowed adequate decision-making for the field campaign observation deployment and the instrument operation. Shortly after the end of the campaign, a first re-analysis with more observations was performed with the first SOP operational software. An ensuing comprehensive second re-analysis of the first SOP which included field research observations (not assimilated in real-time), and some reprocessed observation datasets, was made with AROME-WMED. Moreover, a more recent version of the AROME model was used with updated background error statistics for the assimilation process. This paper depicts the main differences between the real-time version and the benefits brought by HyMeX re-analyses with AROME-WMED. The first re-analysis used 9 % of additional data and the second one 24 % more compared to the real-time version. The second re-analysis is found to be closer to observations than the previous AROME-WMED analyses. The second re-analysis forecast errors of surface parameters are reduced up to the 18-h or 24-h forecast range. In the mid and in the upper troposphere, upper-level fields are also improved up to the 48-h forecast range when compared to radiosondes. Integrated Water Vapour comparisons indicate a positive benefit for at least 24 hours. Precipitation forecasts are found to be improved with the second re-analysis for a thresholds up to 10 mm/24-h. For higher thresholds, the frequency bias is degraded. Finally, improvement brought by the second re-analysis is illustrated with the Intensive Observation Period (IOP 8) associated with heavy precipitation over Eastern Spain and South of France.

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