scholarly journals Estimates of Mode-S EHS aircraft-derived wind observation errors using triple collocation

2016 ◽  
Vol 9 (8) ◽  
pp. 4141-4150 ◽  
Author(s):  
Siebren de Haan
Keyword(s):  
Traffic Control ◽  
Weather Prediction ◽  
Model Error ◽  
Numerical Weather Prediction Model ◽  
Doppler Weather Radar ◽  
Observation Error ◽  
Wind Observation ◽  
Temperature Observation ◽  
Independent Observations ◽  
Wind Measurements

Abstract. Information on the accuracy of meteorological observation is essential to assess the applicability of the measurements. In general, accuracy information is difficult to obtain in operational situations, since the truth is unknown. One method to determine this accuracy is by comparison with the model equivalent of the observation. The advantage of this method is that all measured parameters can be evaluated, from 2 m temperature observation to satellite radiances. The drawback is that these comparisons also contain the (unknown) model error. By applying the so-called triple-collocation method (Stoffelen, 1998), on two independent observations at the same location in space and time, combined with model output, and assuming uncorrelated observations, the three error variances can be estimated. This method is applied in this study to estimate wind observation errors from aircraft, obtained utilizing information from air traffic control surveillance radar with Selective Mode Enhanced Surveillance capabilities (Mode-S EHS, see de Haan, 2011). Radial wind measurements from Doppler weather radar and wind vector measurements from sodar, together with equivalents from a non-hydrostatic numerical weather prediction model, are used to assess the accuracy of the Mode-S EHS wind observations. The Mode-S EHS wind (zonal and meridional) observation error is estimated to be less than 1.4±0.1 m s−1 near the surface and around 1.1 ± 0.3 m s−1 at 500 hPa.

scholarly journals Estimates of Mode-S EHS aircraft derived wind observation errors using triple collocation

2015 ◽  
Vol 8 (12) ◽  
pp. 12633-12661 ◽  
Author(s):  
S. de Haan
Keyword(s):  
Weather Prediction ◽  
Model Error ◽  
Numerical Weather Prediction Model ◽  
Doppler Weather Radar ◽  
Observation Error ◽  
Model Output ◽  
Independent Observation ◽  
Wind Observation ◽  
Temperature Observation ◽  
Wind Measurements

Abstract. Information on the accuracy of meteorological observation is essential to assess the applicability of the measurement. In general, accuracy information is difficult to obtain in operational situations, since the truth is unknown. One method to determine this accuracy is by comparison with model equivalent of the observation. The advantage of this method is that all measured parameters can be evaluated, from two meter temperature observation to satellite radiances. The drawback is that these comparisons contain also the (unknown) model error. By applying the so-called triple collocation method (Stoffelen, 1998), on two independent observation at the same location in space and time, combined with model output, and assuming uncorrelated observations, the three error variances can be estimated. This method is applied in this study to estimate wind observation errors from aircraft, obtained using Mode-S EHS (de Haan, 2011). Radial wind measurements from Doppler weather Radar and wind vector measurements from Sodar, together with equivalents from a non-hydrostatic numerical weather prediction model are used to assess the accuracy of the Mode-S EHS wind observations. The Mode-S EHS wind observation error is estimated to be less than 1.4 ± 0.1 m s−1 near the surface and around 1.1 ± 0.3 m s−1 at 500 hPa.

scholarly journals On Contrasting Ensemble Simulations of Two Great Plains Bow Echoes

2016 ◽  
Vol 31 (3) ◽  
pp. 787-810 ◽  
Author(s):  
John Lawson ◽  
William A. Gallus
Keyword(s):  
Great Plains ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Model Error ◽  
Numerical Weather Prediction Model ◽  
Radius Of Curvature ◽  
Convective Systems ◽  
Bow Echo ◽  
Mesoscale Convective ◽  
Microphysical Parameterization

Abstract Bow echo structures, a subset of mesoscale convective systems (MCSs), are often poorly forecast within deterministic numerical weather prediction model simulations. Among other things, this may be due to the inherent low predictability associated with bow echoes, deficient initial conditions (ICs), and inadequate parameterization schemes. Four different ensemble configurations assessed the sensitivity of the MCSs’ simulated reflectivity and radius of curvature to the following: perturbations in initial and lateral boundary conditions using a global dataset, different microphysical schemes, a stochastic kinetic energy backscatter (SKEB) scheme, and a mix of the previous two. One case is poorly simulated no matter which IC dataset or microphysical parameterization is used. In the other case, almost all simulations reproduce a bow echo. When the IC dataset and microphysical parameterization is fixed within a SKEB ensemble, ensemble uncertainty is smaller. However, while differences in the location and timing of the MCS are reduced, variations in convective mode remain substantial. Results suggest the MCS’s positioning is influenced primarily by ICs, but its mode is most sensitive to the model error uncertainty. Hence, correct estimation of model error uncertainty on the storm scale is crucial for adequate spread and the probabilistic forecast of convective events.

scholarly journals On the model uncertainties in Bayesian source reconstruction using the emission inverse modelling system FREARtool v1.0 and the Lagrangian transport and dispersion model Flexpart v9.0.2

2020 ◽  
Author(s):  
Pieter De Meutter ◽  
Ian Hoffman ◽  
Kurt Ungar
Keyword(s):  
Atmospheric Transport ◽  
Dispersion Model ◽  
Weather Prediction ◽  
Source Parameters ◽  
Model Error ◽  
Observation Error ◽  
Source Reconstruction ◽  
Credible Intervals ◽  
Atmospheric Releases ◽  
Test Ban

Abstract. Bayesian source reconstruction is a powerful tool for determining atmospheric releases. It can be used, amongst other applications, to identify a point source releasing radioactive particles into the atmosphere. This is relevant for applications such as emergency response in case of a nuclear accident, or Comprehensive Nuclear-Test-Ban treaty verification. The method involves solving an inverse problem using environmental radioactivity observations and atmospheric transport models. The Bayesian approach has the advantage of providing credible intervals on the inferred source parameters in a natural way. However, it requires the specification of the inference input errors, such as the observation error and model error. The latter is particularly hard to provide as there is no straightforward way to determine the atmospheric transport and dispersion model error. Here, the importance of model error is illustrated for Bayesian source reconstruction using a recent and unique case where radionuclides were detected on several continents. A numerical weather prediction ensemble is used to create an ensemble of atmospheric transport and dispersion simulations, and a method is proposed to determine the model error.

scholarly journals On the model uncertainties in Bayesian source reconstruction using an ensemble of weather predictions, the emission inverse modelling system FREAR v1.0, and the Lagrangian transport and dispersion model Flexpart v9.0.2

2021 ◽  
Vol 14 (3) ◽  
pp. 1237-1252
Author(s):  
Pieter De Meutter ◽  
Ian Hoffman ◽  
Kurt Ungar
Keyword(s):  
Atmospheric Transport ◽  
Dispersion Model ◽  
Weather Prediction ◽  
Source Parameters ◽  
Model Error ◽  
Observation Error ◽  
Source Reconstruction ◽  
Transport Models ◽  
Atmospheric Releases ◽  
Test Ban

Abstract. Bayesian source reconstruction is a powerful tool for determining atmospheric releases. It can be used, amongst other applications, to identify a point source releasing radioactive particles into the atmosphere. This is relevant for applications such as emergency response in case of a nuclear accident or Comprehensive Nuclear-Test-Ban treaty verification. The method involves solving an inverse problem using environmental radioactivity observations and atmospheric transport models. The Bayesian approach has the advantage of providing an uncertainty quantification on the inferred source parameters. However, it requires the specification of the inference input errors, such as the observation error and model error. The latter is particularly hard to provide as there is no straightforward way to determine the atmospheric transport and dispersion model error. Here, the importance of model error is illustrated for Bayesian source reconstruction using a recent and unique case where radionuclides were detected on several continents. A numerical weather prediction ensemble is used to create an ensemble of atmospheric transport and dispersion simulations, and a method is proposed to determine the model error.

scholarly journals Validation of wind measurements of two mesosphere–stratosphere–troposphere radars in northern Sweden and in Antarctica

2021 ◽  
Vol 14 (4) ◽  
pp. 2813-2825
Author(s):  
Evgenia Belova ◽  
Peter Voelger ◽  
Sheila Kirkwood ◽  
Susanna Hagelin ◽  
Magnus Lindskog ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Radar Data ◽  
Numerical Weather Prediction Model ◽  
Mean Difference ◽  
Research Station ◽  
Random Errors ◽  
Meridional Winds ◽  
Wind Measurements ◽  
The Mean ◽  
High Level

Abstract. Two atmospheric VHF radars: ESRAD (Esrange MST radar) located near Kiruna in the Swedish Arctic and MARA (Moveable Atmospheric Radar for Antarctica) at the Indian research station Maitri in Antarctica perform wind measurements in the troposphere and lower stratosphere on a regular basis. We compared horizontal winds at altitudes between about 0.5 and 14 km derived from the radar data using the full correlation analysis (FCA) technique with radiosonde observations and models. The comparison with 28 radiosondes launched from January 2017 to August 2019 showed that ESRAD underestimates the zonal and meridional winds by about 8 % and 25 %, respectively. This is likely caused by the receiver group arrangement used for the FCA together with a high level of non-white noise. A similar result was found when comparing with the regional numerical weather prediction model HARMONIE-AROME (Bengtsson et al., 2017) for the period September 2018–May 2019. The MARA winds were compared with winds from radiosondes for the period February–October 2014 (291 occasions). In contrast to ESRAD, there is no indication that MARA underestimates the winds compared to the sondes. The mean difference between the radar and radiosonde winds is close to zero for both zonal and meridional components. The comparison of MARA with the ECMWF ERA5 reanalysis for January–December 2019 reveals good agreement with the mean difference between 0.1 and −0.5 m/s depending on the component and season. The random errors in the wind components (standard deviations over all estimates in 1 h averages) are typically 2–3 m/s for both radars. Standard deviation of the differences between radars and sondes are 3–5 m/s.

scholarly journals Representing Model Uncertainty for Global Atmospheric CO<sub>2</sub> Flux Inversions Using ECMWF-IFS-46R1

2020 ◽  
Author(s):  
Joe McNorton ◽  
Nicolas Bousserez ◽  
Anna Agustí-Panareda ◽  
Gianpaolo Balsamo ◽  
Margarita Choulga ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Co2 Flux ◽  
Model Error ◽  
Numerical Weather Prediction Model ◽  
Forward Model ◽  
Concentration Gradients ◽  
Near Surface ◽  
Surface Transport ◽  
Atmospheric Flux ◽  
Error Standard Deviation

Abstract. Atmospheric flux inversions use observations of atmospheric CO2 to provide anthropogenic and biogenic CO2 flux estimates at a range of spatiotemporal scales. Inversions require prior flux, forward model and observation errors to estimate posterior fluxes and uncertainties. We use a numerical weather prediction model to diagnose the global forward model error associated with uncertainties in the initial meteorological state, physical parameterisations and in-model biogenic response to meteorological uncertainty. We then compare the error with the atmospheric response to uncertainty in the prior anthropogenic emissions. Although transport errors are variable, average total column CO2 (XCO2) transport errors over anthropogenic emission hotspots (0.1–0.8 ppm) are comparable to, and often exceed prior monthly anthropogenic flux uncertainties project onto the same space (0.1–1.4 ppm). Average near-surface transport error at 3 sites (Paris, Caltech and Tsukuba) range from 1.7–7.2 ppm. The global average XCO2 transport error standard deviation plateaus at ~0.1 ppm after 2–3 days, after which atmospheric mixing significantly dampens the concentration gradients. Error correlations are found to be highly flow-dependent, with XCO2 spatiotemporal correlation length scales ranging from 0 km to 700 km and 0 to 260 minutes. Globally, the average model error caused by the biogenic response to atmospheric meteorological uncertainties is small (

scholarly journals Global 3D Features of Error Variances of GPS Radio Occultation and Radiosonde Observations

2020 ◽  
Vol 13 (1) ◽  
pp. 1
Author(s):  
Xu Xu ◽  
Xiaolei Zou
Keyword(s):  
Radio Occultation ◽  
Weather Prediction ◽  
Lower Troposphere ◽  
Error Variance ◽  
Specific Humidity ◽  
Temperature Error ◽  
Observation Error ◽  
Bending Angle ◽  
Gps Radio Occultation ◽  
Low Latitudes

Global Positioning System (GPS) radio occultation (RO) and radiosonde (RS) observations are two major types of observations assimilated in numerical weather prediction (NWP) systems. Observation error variances are required input that determines the weightings given to observations in data assimilation. This study estimates the error variances of global GPS RO refractivity and bending angle and RS temperature and humidity observations at 521 selected RS stations using the three-cornered hat method with additional ERA-Interim reanalysis and Global Forecast System forecast data available from 1 January 2016 to 31 August 2019. The global distributions, of both RO and RS observation error variances, are analyzed in terms of vertical and latitudinal variations. Error variances of RO refractivity and bending angle and RS specific humidity in the lower troposphere, such as at 850 hPa (3.5 km impact height for the bending angle), all increase with decreasing latitude. The error variances of RO refractivity and bending angle and RS specific humidity can reach about 30 N-unit2, 3 × 10−6 rad2, and 2 (g kg−1)2, respectively. There is also a good symmetry of the error variances of both RO refractivity and bending angle with respect to the equator between the Northern and Southern Hemispheres at all vertical levels. In this study, we provide the mean error variances of refractivity and bending angle in every 5°-latitude band between the equator and 60°N, as well as every interval of 10 hPa pressure or 0.2 km impact height. The RS temperature error variance distribution differs from those of refractivity, bending angle, and humidity, which, at low latitudes, are smaller (less than 1 K2) than those in the midlatitudes (more than 3 K2). In the midlatitudes, the RS temperature error variances in North America are larger than those in East Asia and Europe, which may arise from different radiosonde types among the above three regions.

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