scholarly journals Coupling a weather model directly to GNSS orbit determination

2021 ◽  
Author(s):  
Angel Navarro Trastoy ◽  
Sebastian Strasser ◽  
Lauri Tuppi ◽  
Maksym Vasiuta ◽  
Markku Poutanen ◽  
...  
Keyword(s):  
Orbit Determination ◽  
Prediction Models ◽  
Weather Prediction ◽  
Direct Coupling ◽  
Atmospheric Effects ◽  
Satellite Orbits ◽  
Small Data ◽  
Neutral Atmosphere ◽  
Data Set ◽  
Weather Model

Abstract. Neutral atmosphere bends and delays propagation of microwave signals in satellite-based navigation. Weather prediction models can be used to estimate these effects by providing 3-dimensional refraction fields to estimate signal delay in the zenith direction and determine a low-dimensional mapping of this delay to desired azimuth and elevation angles. In this study, a global numerical weather prediction model (OpenIFS licensed for Academic use by ECMWF) is used to generate the refraction fields. The ray-traced slant delays are supplied as such – in contrast to mapping – for an orbit solver (GROOPS software toolkit of TUG) which applies the raw observation method. Here we show that such a close coupling is possible without need for major additional modifications in the solver codes. The main finding here is that the adopted approach provides a very good a priori model for the atmospheric effects on navigation signals, as measured with the midnight discontinuity of GNSS satellite orbits. Our interpretation is that removal of the intermediate mapping step allows to take advantage of the local refraction field asymmetries in the GNSS signal processing. Moreover, the direct coupling helps in identifying deficiencies in the slant delay computation because the modelling errors are not convoluted in the precision-reducing mapping. These conclusions appear robust, despite the relatively small data set of raw code and phase observations covering the core network of 66 ground-based stations of the International GNSS Service over one-month periods in December 2016 and June 2017. More generally, the new configuration enhances our control of geodetic and meteorological aspects of the orbit problem. This is pleasant because we can, for instance, regulate at will the weather model output frequency and increase coverage of spatio-temporal aspects of weather variations. The direct coupling of a weather model in precise GNSS orbit determination presented in this paper provides a unique framework for benefiting even more widely than previously the apparent synergies in space geodesy and meteorology.

scholarly journals An over-land aerosol optical depth data set for data assimilation by filtering, correction, and aggregation of MODIS Collection 5 optical depth retrievals

2010 ◽  
Vol 3 (5) ◽  
pp. 4091-4167 ◽  
Author(s):  
E. J. Hyer ◽  
J. S. Reid ◽  
J. Zhang
Keyword(s):  
Data Assimilation ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Prediction Models ◽  
Hot Spot ◽  
Weather Prediction ◽  
Surface Boundary ◽  
Data Set ◽  
Modis Aod ◽  
Level 3

Abstract. MODIS Collection 5 retrieved aerosol optical depth (AOD) over land (MOD04/MYD04) was evaluated using 4 years of matching AERONET observations, to assess its suitability for aerosol data assimilation in numerical weather prediction models. Examination of errors revealed important sources of variation in random errors (e.g., atmospheric path length, scattering angle "hot spot"), and systematic biases (e.g., snow and cloud contamination, surface albedo bias). A set of quality assurance (QA) filters was developed to avoid conditions with potential for significant AOD error. An empirical correction for surface boundary condition using the MODIS 16-day albedo product captured 25% of the variability in the site mean bias at low AOD. A correction for regional microphysical bias using the AERONET fine/coarse partitioning information increased the global correlation between MODIS and AERONET from r2=0.62–0.65 to r2=0.71–0.73. Application of these filters and corrections improved the global fraction of MODIS AOD within (0.05±20%) of AERONET to 77%, up from 67% using only built-in MODIS QA. The compliant fraction in individual regions was improved by as much as 20% (South America). An aggregated Level 3 product for use in a data assimilation system is described, along with a prognostic error model to estimate uncertainties on a per-observation basis. The new filtered and corrected Level 3 product has improved performance over built-in MODIS QA with less than a 15% reduction in overall data available for data assimilation.

scholarly journals Sensing small-scale structures in the troposphere with tomographic principles (IAG working group)

2020 ◽  
Author(s):  
Gregor Moeller ◽  
Chi Ao ◽  
Zohreh Adavi ◽  
Hugues Brenot ◽  
André Sá ◽  
...  
Keyword(s):  
Working Group ◽  
Data Exchange ◽  
Prediction Models ◽  
International Association ◽  
Weather Prediction ◽  
Measurement Techniques ◽  
Global Navigation Satellite Systems ◽  
Lower Atmosphere ◽  
Small Scale ◽  
Neutral Atmosphere

<p>Within the International Association of Geodesy (IAG), a new working group was formed with the intention to bring together researchers and professionals working on tomography-based concepts for sensing the neutral atmosphere with space-geodetic techniques. Hereby the focus lies on Global Navigation Satellite Systems (GNSS) but also on complementary observation techniques, like Interferometric Synthetic Aperature Radar (InSAR) or microwave radiometers, sensitive to the water vapor distribution in the lower atmosphere.</p><p>In the next four years (2019-2023), we will address current challenges in tropospheric tomography with focus on ground-based and space-based measurements, the combination of measurement techniques and the design of new observation concepts using tomographic principles. While geodetic GNSS networks are nowadays the backbone for troposphere tomography studies, further local densifications, e.g. at airports, cities or fundamental stations are necessary to achieve very fine spatial and temporal resolution. Besides, the combination of ground-based GNSS with other microwave techniques like radio occultation or InSAR seems to be beneficial due their complementary nature. Therefore, several further developments in the field of tropospheric tomography are required. This includes more dynamical tomography models - adaptable to varying input data, advanced ray-tracing algorithms for the reconstruction of space-based observations and the coordination of a benchmark campaign.</p><p>In this presentation, we will give an overview about the current challenges in tropospheric tomography and the objectives of working group. The latter will also include standards for data exchange and therefore, make tomographic products available for the assimilation into numerical weather prediction models but also for various other disciplines, which rely on accurate wet refractivities or derived products like tropospheric signal delays.</p>

scholarly journals The S2M meteorological and snow cover reanalysis over the French mountainous areas, description and evaluation (1958–2020)

2021 ◽  
Author(s):  
Matthieu Vernay ◽  
Matthieu Lafaysse ◽  
Diego Monteiro ◽  
Pascal Hagenmuller ◽  
Rafife Nheili ◽  
...  
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Prediction Models ◽  
Weather Prediction ◽  
Scientific Publications ◽  
Mountain Areas ◽  
Data Set ◽  
Meteorological Observations ◽  
Surface Scheme

Abstract. This work introduces the S2M (SAFRAN - SURFEX/ISBA-Crocus - MEPRA) meteorological and snow cover reanalysis in the French Alps, Pyrenees and Corsica, spanning the time period from 1958 to 2020. The simulations are made over elementary areas, referred to as massifs, designed to represent the main drivers of the spatial variability observed in mountain ranges (elevation, slope and aspect). The meteorological reanalysis is performed by the SAFRAN system, which combines information from numerical weather prediction models (ERA-40 reanalysis from 1958 to 2002, ARPEGE from 2002 to 2020) and the best possible set of available in-situ meteorological observations. SAFRAN outputs are used to drive the Crocus detailed snow cover model, which is part of the land surface scheme SURFEX/ISBA. This model chain provides simulations of the evolution of the snow cover, underlying ground, and the associated avalanche hazard using the MEPRA model. This contribution describes and discusses the main climatological characteristics (climatology, variability and trends), and the main limitations of this dataset. We provide a short overview of the scientific applications using this reanalysis in various scientific fields related to meteorological conditions and the snow cover in mountain areas. An evaluation of the skill of S2M is also displayed, in particular through comparison to 665 independent in-situ snow depth observations. Further, we describe the technical handling of this open access data set, available at this address: http://dx.doi.org/10.25326/37#v2020.2. Scientific publications using this dataset must mention in the acknowledgments: "The S2M data are provided by Météo-France - CNRS, CNRM Centre d’Etudes de la Neige, through AERIS" and refer to it as Vernay et al. (2020).

scholarly journals Ensemble prediction of floods – catchment non-linearity and forecast probabilities

2007 ◽  
Vol 7 (4) ◽  
pp. 431-444 ◽  
Author(s):  
J. Komma ◽  
C. Reszler ◽  
G. Blöschl ◽  
T. Haiden
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Ensemble Methods ◽  
Rain Gauge ◽  
Ensemble Prediction ◽  
Lead Times ◽  
Forecast Errors ◽  
Ensemble Spread ◽  
Relative Operating Characteristic ◽  
Data Set

Abstract. Quantifying the uncertainty of flood forecasts by ensemble methods is becoming increasingly important for operational purposes. The aim of this paper is to examine how the ensemble distribution of precipitation forecasts propagates in the catchment system, and to interpret the flood forecast probabilities relative to the forecast errors. We use the 622 km2 Kamp catchment in Austria as an example where a comprehensive data set, including a 500 yr and a 1000 yr flood, is available. A spatially-distributed continuous rainfall-runoff model is used along with ensemble and deterministic precipitation forecasts that combine rain gauge data, radar data and the forecast fields of the ALADIN and ECMWF numerical weather prediction models. The analyses indicate that, for long lead times, the variability of the precipitation ensemble is amplified as it propagates through the catchment system as a result of non-linear catchment response. In contrast, for lead times shorter than the catchment lag time (e.g. 12 h and less), the variability of the precipitation ensemble is decreased as the forecasts are mainly controlled by observed upstream runoff and observed precipitation. Assuming that all ensemble members are equally likely, the statistical analyses for five flood events at the Kamp showed that the ensemble spread of the flood forecasts is always narrower than the distribution of the forecast errors. This is because the ensemble forecasts focus on the uncertainty in forecast precipitation as the dominant source of uncertainty, and other sources of uncertainty are not accounted for. However, a number of analyses, including Relative Operating Characteristic diagrams, indicate that the ensemble spread is a useful indicator to assess potential forecast errors for lead times larger than 12 h.

scholarly journals An over-land aerosol optical depth data set for data assimilation by filtering, correction, and aggregation of MODIS Collection 5 optical depth retrievals

2011 ◽  
Vol 4 (3) ◽  
pp. 379-408 ◽  
Author(s):  
E. J. Hyer ◽  
J. S. Reid ◽  
J. Zhang
Keyword(s):  
Data Assimilation ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Prediction Models ◽  
Hot Spot ◽  
Weather Prediction ◽  
Surface Boundary ◽  
Data Set ◽  
Modis Aod ◽  
Level 3

Abstract. MODIS Collection 5 retrieved aerosol optical depth (AOD) over land (MOD04/MYD04) was evaluated using 4 years of matching AERONET observations, to assess its suitability for aerosol data assimilation in numerical weather prediction models. Examination of errors revealed important sources of variation in random errors (e.g., atmospheric path length, scattering angle "hot spot"), and systematic biases (e.g., snow and cloud contamination, surface albedo bias). A set of quality assurance (QA) filters was developed to avoid conditions with potential for significant AOD error. An empirical correction for surface boundary condition using the MODIS 16-day albedo product captured 25% of the variability in the site mean bias at low AOD. A correction for regional microphysical bias using the AERONET fine/coarse partitioning information increased the global correlation between MODIS and AERONET from r2 = 0.62–0.65 to r2 = 0.71–0.73. Application of these filters and corrections improved the global fraction of MODIS AOD within (0.05 ± 20%) of AERONET to 77%, up from 67% using only built-in MODIS QA. The compliant fraction in individual regions was improved by as much as 20% (South America). An aggregated Level 3 product for use in a data assimilation system is described, along with a prognostic error model to estimate uncertainties on a per-observation basis. The new filtered and corrected Level 3 product has improved performance over built-in MODIS QA with less than a 15% reduction in overall data available for data assimilation.

scholarly journals Sensitivity of GNSS tropospheric gradients to processing options

2018 ◽  
Author(s):  
Michal Kačmařík ◽  
Jan Douša ◽  
Florian Zus ◽  
Pavel Václavovic ◽  
Kyriakos Balidakis ◽  
...  
Keyword(s):  
Real Time ◽  
Prediction Models ◽  
Weather Prediction ◽  
Gradient Estimates ◽  
Post Processing ◽  
Data Set ◽  
Processing Mode ◽  
Gradient Mapping ◽  
The Impact

Abstract. An analysis of processing settings impact on estimated tropospheric gradients is presented. The study is based on the benchmark data set collected within the COST GNSS4SWEC action with observations from 430 GNSS reference stations in central Europe for May and June 2013. Tropospheric gradients were estimated in eight different variants of GNSS data processing using Precise Point Positioning with the G-Nut/Tefnut software. The impact of the gradient mapping function, elevation cut-off angle, GNSS constellation and real-time versus post-processing mode were assessed by comparing the variants by each to other and by evaluating them with respect to tropospheric gradients derived from two numerical weather prediction models. Generally, all the solutions in the post-processing mode provided a robust tropospheric gradient estimation with a clear relation to real weather conditions. The quality of tropospheric gradient estimates in real-time mode mainly depends on the actual quality of the real-time orbits and clocks. Best results were achieved using the 3° elevation angle cut-off and a combined GPS + GLONASS constellation. Systematic effects of up to 0.3 mm were observed in estimated tropospheric gradients when using different gradient mapping functions which depend on the applied observation elevation-dependent weighting. While the latitudinal troposphere tilting causes a systematic difference in the north gradient component on a global scale, large local wet gradients pointing to a direction of increased humidity cause systematic differences in both gradient components depending on the gradient direction.

scholarly journals Homogenizing surface pressure time-series from operational numerical weather prediction models for geodetic applications

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
H. Dobslaw
Keyword(s):  
Time Series ◽  
Surface Pressure ◽  
Prediction Models ◽  
Weather Prediction ◽  
Time Consistency ◽  
Reanalysis Data ◽  
Data Set ◽  
Numerical Weather ◽  
Common Reference ◽  
Pressure Time

AbstractGlobal surface pressure grids from 14.5 years of 6-hourly analyses out of both the operational ECMWF weather prediction model and ERA-Interim are mapped to a common reference orography by means of ECMWF’s mean sea-level pressure diagnostic. The approach reduces both relative biases and residual variability by about one order of magnitude and thereby achieves a consistency among both data sets at the level of about 1 hPa. Remaining differences rather reflect temperature biases and also resolution limitations of the reanalysis data set, but are not anymore related to the local roughness in orography or to changes in the spatial resolution of the operational model. The presented reduction method therefore allows to obtain surface pressure time series with the long-time consistency of a reanalysis from an operational numerical weather model with much higher resolution and much shorter latency, making the results suitable for geodetic near realtime applications requiring continuously updated time series that are homogeneous over many years.

scholarly journals Corrections of Molecular Morphology and Hydrogen Bond for Improved Crystal Density Prediction

Molecules ◽  
2019 ◽  
Vol 25 (1) ◽  
pp. 161 ◽  
Author(s):  
Linyuan Wang ◽  
Miao Zhang ◽  
Jie Chen ◽  
Liang Su ◽  
Shicao Zhao ◽  
...  
Keyword(s):  
Hydrogen Bond ◽  
Energetic Materials ◽  
Prediction Models ◽  
Molecular Design ◽  
Small Data ◽  
Detonation Pressure ◽  
Data Set ◽  
Density Prediction ◽  
Crystal Density ◽  
Molecular Morphology

Density prediction is of great significance for molecular design of energetic materials, since detonation velocity linearly with density and detonation pressure increases with the density squared. However, the accuracy and generalization of former reported prediction models need further improvement, because most of them are derived from small data sets and few molecular descriptors. As shown in this paper, for molecules presenting brick-like shape or containing more hydrogen-bond donors the predicted densities have large negative deviations from experimental values. Thus, a molecular morphology descriptor η and a hydrogen-bond descriptor Hb are introduced as correction items to build 3 new QSPR models. Besides, 3694 nitro compounds are adopted as data set by this work. The accuracies are obviously improved, and the generalizations are verified by an independent test set. At the level of B3PW91/6-31G(d,p), the effective ratios (ERs) of the 3 Equations, for Δρ < 5%, are 92.7%, 91.8%, and 93.3%; for Δρ < 2%, the values are 53.5%, 51.3%, and 54.7%. At the level of B3LYP/6-31G**, for Δρ < 5%, the values are 92.3%, 91.4% and 92.9%; for Δρ < 2%, the values are 53.7%, 51.4% and 53.2%.

scholarly journals Assimilation of SCATSAR Soil Wetness Index in SURFEX 8.0 to improve weather forecasts

2019 ◽  
Author(s):  
Stefan Schneider ◽  
Bernhard Bauer-Marschallinger
Keyword(s):  
Soil Moisture ◽  
Prediction Models ◽  
Surface Soil ◽  
Weather Prediction ◽  
Soil Layer ◽  
New Combination ◽  
Data Set ◽  
Weather Forecasts ◽  
Soil Moisture Data ◽  
Soil Model

Abstract. To date, in numerical weather prediction models it has only been possible to assimilate surface soil moisture data. Due to the structure of this surface soil layer in soil models, the effect of the assimilation vanishes fast. This results in a small impact on model performance. Here we present a combination of two new developments to overcome this problem. On the one hand, a new satellite based soil moisture data set is assimilated that combines the advantages of two different sensors (MetOp ASCAT and Sentinel-1 SAR) and the so-called T-value approach to estimate the soil moisture content of deeper soil layers. On the other hand, an advanced version of the well-established SURFEX soil model data assimilation software is used to ingest this new data source for improved short-range weather forecasts. Comparisons of the two data sets from satellite and soil model indicate that the estimation of deep soil moisture from superficial measurements produces reasonable estimates down to 0.5 m. Assimilation experiments with a simplified Extended Kalman Filter for a model domain covering Austria shows the benefit of this new combination with improved verification scores for temperature and relative humidity forecasts at 2 m above ground.

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