The Three-Cornered Hat Method for Estimating Error Variances of Three or More Atmospheric Data Sets – Part II: Evaluating Radio Occultation and Radiosonde Observations, Global Model Forecasts, and Reanalyses

2021 ◽  
Author(s):  
Therese Rieckh ◽  
Jeremiah P. Sjoberg ◽  
Richard A. Anthes
Keyword(s):  
Radio Occultation ◽  
State Of The Art ◽  
Specific Humidity ◽  
Data Sets ◽  
Error Growth ◽  
Atmospheric Conditions ◽  
Error Statistics ◽  
Weather And Climate ◽  
Atmospheric Data ◽  
The Impact

AbstractWe apply the three-cornered hat (3CH) method to estimate refractivity, bending angle, and specific humidity error variances for a number of data sets widely used in research and/or operations: radiosondes, radio occultation (COSMIC, COSMIC-2), NCEP global forecasts, and nine reanalyses. We use a large number and combinations of data sets to obtain insights into the impact of the error correlations among different data sets that affect 3CH estimates. Error correlations may be caused by actual correlations of errors, representativeness differences, or imperfect co-location of the data sets. We show that the 3CH method discriminates among the data sets and how error statistics of observations compare to state-of-the-art reanalyses and forecasts, as well as reanalyses that do not assimilate satellite data. We explore results for October and November 2006 and 2019 over different latitudinal regions and show error growth of the NCEP forecasts with time. Because of the importance of tropospheric water vapor to weather and climate, we compare error estimates of refractivity for dry and moist atmospheric conditions.

scholarly journals Outlier Detection Based on Residual Histogram Preference for Geometric Multi-Model Fitting

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3037
Author(s):  
Xi Zhao ◽  
Yun Zhang ◽  
Shoulie Xie ◽  
Qianqing Qin ◽  
Shiqian Wu ◽  
...  
Keyword(s):  
Outlier Detection ◽  
State Of The Art ◽  
Geometric Model ◽  
Model Fitting ◽  
Distribution Model ◽  
Data Sets ◽  
Analysis Method ◽  
Detection Scheme ◽  
The Impact ◽  
Better Than

Geometric model fitting is a fundamental issue in computer vision, and the fitting accuracy is affected by outliers. In order to eliminate the impact of the outliers, the inlier threshold or scale estimator is usually adopted. However, a single inlier threshold cannot satisfy multiple models in the data, and scale estimators with a certain noise distribution model work poorly in geometric model fitting. It can be observed that the residuals of outliers are big for all true models in the data, which makes the consensus of the outliers. Based on this observation, we propose a preference analysis method based on residual histograms to study the outlier consensus for outlier detection in this paper. We have found that the outlier consensus makes the outliers gather away from the inliers on the designed residual histogram preference space, which is quite convenient to separate outliers from inliers through linkage clustering. After the outliers are detected and removed, a linkage clustering with permutation preference is introduced to segment the inliers. In addition, in order to make the linkage clustering process stable and robust, an alternative sampling and clustering framework is proposed in both the outlier detection and inlier segmentation processes. The experimental results also show that the outlier detection scheme based on residual histogram preference can detect most of the outliers in the data sets, and the fitting results are better than most of the state-of-the-art methods in geometric multi-model fitting.

Assimilation of GPS Radio Occultation Refractivity Data from CHAMP and SAC-C Missions over High Southern Latitudes with MM5 4DVAR

2008 ◽  
Vol 136 (8) ◽  
pp. 2923-2944 ◽  
Author(s):  
Tae-Kwon Wee ◽  
Ying-Hwa Kuo ◽  
David H. Bromwich ◽  
Andrew J. Monaghan
Keyword(s):  
Radio Occultation ◽  
Ross Sea ◽  
Mesoscale Model ◽  
Positive Impact ◽  
Extended Period ◽  
Error Reduction ◽  
Specific Humidity ◽  
Subtle Change ◽  
Gps Radio Occultation ◽  
The Impact

Abstract In this study, the GPS radio occultation (RO) data from the Challenging Minisatellite Payload (CHAMP) and Satellite de Aplicaciones Cientificas-C (SAC-C) missions are assimilated. An updated version of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) four-dimensional variational data assimilation system (4DVAR) is used to assess the impact of the GPS RO data on analyses and short-range forecasts over the Antarctic. The study was performed during the period of intense cyclonic activity in the Ross Sea, 9–19 December 2001. On average 66 GPS RO soundings were assimilated daily. For the assimilation over a single 12-h period, the impact of GPS RO data was only marginally positive or near neutral, and it varied markedly from one 12-h period to another. The large case-to-case variation was attributed to the low number of GPS RO soundings and a strong dependency of forecast impact on the location of the soundings relative to the rapidly developing cyclone. Despite the moderate general impact, noticeable reduction of temperature error in the upper troposphere and lower stratosphere was found, which demonstrates the value of GPS RO data in better characterizing the tropopause. Significant error reduction was also noted in geopotential height and wind fields in the stratosphere. Those improvements indicate that early detection of the upper-level precursors for storm development is a potential benefit of GPS RO data. When the assimilation period was extended to 48 h, a considerable positive impact of GPS RO data was found. All parameters that were investigated (i.e., temperature, pressure, and specific humidity) showed the positive impact throughout the entire model atmosphere for forecasts extending up to 5 days. The impact increased in proportion to the length of the assimilation period. Although the differences in the analyses as a result of GPS RO assimilation were relatively small initially, the subtle change and subsequent nonlinear growth led to noticeable forecast improvements at longer ranges. Consequently, the positive impact of GPS RO data was more evident in longer-range (e.g., greater than 2 days) forecasts. A correlation coefficient is introduced to quantify the linear relationship between the analysis errors without GPS RO assimilation and the analysis increments induced by GPS RO assimilation. This measure shows that the growth of GPS RO–induced modifications over time is related to the prominent error reduction observed in GPS RO experiments. The measure may also be useful for understanding how cycling analysis accumulates the positive impact of GPS RO data for an extended period of assimilation.

scholarly journals The impact of atmospheric dispersion in the performance of high-resolution spectrographs

2019 ◽  
Vol 491 (3) ◽  
pp. 3515-3522 ◽  
Author(s):  
B Wehbe ◽  
A Cabral ◽  
J H C Martins ◽  
P Figueira ◽  
N C Santos ◽  
...  
Keyword(s):  
High Resolution ◽  
Flux Distribution ◽  
State Of The Art ◽  
Atmospheric Dispersion ◽  
Atmospheric Conditions ◽  
Scientific Objective ◽  
Dependent Effect ◽  
Astronomical Observations ◽  
Current State ◽  
The Impact

ABSTRACT Differential atmospheric dispersion is a wavelength-dependent effect introduced by the atmosphere. It is one of the instrumental errors that can affect the position of the target as perceived on the sky and its flux distribution. This effect will affect the results of astronomical observations if not corrected by an atmospheric dispersion corrector (ADC). In high-resolution spectrographs, in order to reach a radial velocity (RV) precision of 10 cm s−1, an ADC is expected to return residuals at only a few tens of milliarcseconds (mas). In fact, current state-of-the-art spectrograph conservatively require this level of residuals, although no work has been done to quantify the impact of atmospheric dispersion. In this work, we test the effect of atmospheric dispersion on astronomical observations in general, and in particular on RV precision degradation and flux losses. Our scientific objective was to quantify the amount of residuals needed to fulfil the requirements set on an ADC during the design phase. We found that up to a dispersion of 100 mas, the effect on the RV is negligible. However, on the flux losses, such a dispersion can create a loss of ∼2 per cent at 380 nm, a significant value when efficiency is critical. The requirements set on ADC residuals should take into consideration the atmospheric conditions where the ADC will function, and also all the aspects related with not only the RV precision requirements but also the guiding camera used, the tolerances on the flux loss, and the different melt data of the chosen glasses.

scholarly journals Global temperature estimates in the troposphere and stratosphere: a validation study of COSMIC/FORMOSAT-3 measurements

2008 ◽  
Vol 8 (3) ◽  
pp. 8327-8355 ◽  
Author(s):  
P. Kishore ◽  
S. P. Namboothiri ◽  
J. H. Jiang ◽  
V. Sivakumar ◽  
K. Igarashi
Keyword(s):  
Radio Occultation ◽  
Specific Humidity ◽  
First Year ◽  
Data Sets ◽  
Tropical Tropopause ◽  
The United Kingdom ◽  
Tropopause Region ◽  
Environmental Prediction ◽  
Humidity Profiles ◽  
Good Agreement

Abstract. This paper mainly focuses on the validation of temperature estimates derived with the newly launched Constellation Observing System for Meteorology Ionosphere and Climate (COSMIC)/Formosa Satellite 3 (FORMOSAT-3) system. The analysis is based on the radio occultation (RO) data sample collected during the first year observation from April 2006 to April 2007. For the validation, we have used the operational stratospheric analyses (models) including the National Centers for Environmental Prediction-Reanalysis (NCEP-Reanalysis), the Japanese 25-year Reanalysis (JRA-25), and the United Kingdom Met Office (MetO) data sets. Comparisons done in different formats reveal excellent agreement between the COSMIC and model outputs. Spatially, the largest deviations are noted in the polar latitudes, and height-wise, the tropical tropopause region noted the maximum differences. However, these differences are only 2–4 K. We found that among the three models the NCEP data sets have the best resemblance with the COSMIC measurements. We also have done comparison of specific humidity and refractivity profiles with other measurements/models. Specific humidity profiles show comparatively large differences at altitudes below 5 km. Refractivity profiles derived by the COSMIC and other datasets show very good agreement.

scholarly journals Adjusting for Publication Bias in JASP — Selection Models and Robust Bayesian Meta-Analysis

2020 ◽  
Author(s):  
František Bartoš ◽  
Maximilian Maier ◽  
Eric-Jan Wagenmakers
Keyword(s):  
Publication Bias ◽  
State Of The Art ◽  
Meta Analysis ◽  
Data Sets ◽  
Selection Models ◽  
The Impact

Meta-analysis is essential for cumulative science, but its validity is compromised by publication bias. In order to mitigate the impact of publication bias, one may apply selection models, which estimate the degree to which non-significant studies are suppressed. Implemented in JASP, these methods allow researchers without programming experience to conduct state-of-the-art publication bias adjusted meta-analysis. In this tutorial, we demonstrate how to conduct a publication bias adjusted meta-analysis in JASP and interpret the results. First, we explain how frequentist selection models correct for publication bias. Second, we introduce Robust Bayesian Meta-Analysis (RoBMA), a Bayesian extension of the frequentist selection models. We illustrate the methodology with two data sets and discuss the interpretation of the results. In addition, we include example text to provide concrete guidance on reporting the meta-analytic results in an academic article. Finally, three tutorial videos are available at https://tinyurl.com/y4g2yodc.

scholarly journals The Three-Cornered Hat Method for Estimating Error Variances of Three or More Atmospheric Data Sets – Part I: Overview and Evaluation

2020 ◽  
Author(s):  
Jeremiah P. Sjoberg ◽  
Richard A. Anthes ◽  
Therese Rieckh
Keyword(s):  
Sample Size ◽  
Historical Development ◽  
Simulated Data ◽  
Data Sets ◽  
Vertical Resolution ◽  
Random Errors ◽  
Atmospheric Data ◽  
Similarities And Differences ◽  
The Impact ◽  
Simulated Data Sets

AbstractThe three-cornered hat (3CH) method, which was originally developed to assess the random errors of atomic clocks, is a means for estimating the error variances of three different data sets. Here we give an overview of the historical development of the 3CH and select other methods for estimating error variances that use either two or three data sets. We discuss similarities and differences between these methods and the 3CH method.This study assesses the sensitivity of the 3CH method to the factors that limit its accuracy, including sample size, outliers, different magnitudes of errors between the data sets, biases, and unknown error correlations. Using simulated data sets for which the errors and their correlations among the data sets are known, this analysis shows the conditions under which the 3CH method provides the most and least accurate estimates. The effect of representativeness errors caused by differences in vertical resolution of data sets is investigated. These representativeness errors are generally small relative to the magnitude of the random errors in the data sets, and the impact of this source of errors can be reduced by appropriate filtering.

Validating the Land-Atmosphere Coupling Behavior in Weather and Climate Models Using Observationally-Based Global Products

2021 ◽  
Author(s):  
Abedeh Abdolghafoorian ◽  
Paul A. Dirmeyer
Keyword(s):  
Heat Flux ◽  
Climate Models ◽  
Heat Waves ◽  
Sensible Heat Flux ◽  
Surface Fluxes ◽  
Heat Fluxes ◽  
Data Sets ◽  
Observational Uncertainty ◽  
Weather And Climate ◽  
The Impact

AbstractThe interactions between land and atmosphere (with terrestrial and atmospheric coupling segments) play a significant role in weather and climate. A predominant segment of land-atmosphere (L-A) feedbacks is the coupling between soil moisture (SM) and surface heat fluxes, the terrestrial coupling leg. The lack of high-quality long-term globally distributed observations, however, has hindered a robust, realistic identification of the terrestrial leg strength on a global scale. This exploratory study provides insight into how SM signals are translated into surface flux signals through the construction of a global depiction of the terrestrial leg from several recently developed global, gridded, observationally- and satellite-based data sets. The feasibility of producing global gridded estimates of L-A coupling metrics is explored. Five weather and climate models used for subseasonal to seasonal forecasting are confronted with the observational estimates to discern discrepancies that may affect their ability to predict phenomena related to L-A feedbacks, such as drought or heat waves. The terrestrial feedback leg from observations corroborates the “hot spots” of L-A coupling found in modeling studies, but the variances in daily time series of surface fluxes differ markedly. Better agreement and generally higher confidence are seen in metrics using latent heat flux than sensible heat flux. Observational metrics allow for clear stratification of model fidelity that is consistent across seasons, despite observational uncertainty. The results highlight the impact of SM on partitioning available surface energy and illustrate the potential of global observationally-based data sets for the assessment of such relationships in weather and climate models.

Ensemble Kalman Filter Data Assimilation in a 1D Numerical Model Used for Fog Forecasting

2010 ◽  
Vol 138 (5) ◽  
pp. 1792-1810 ◽  
Author(s):  
Samuel Rémy ◽  
Thierry Bergot
Keyword(s):  
Kalman Filter ◽  
Numerical Model ◽  
Ensemble Kalman Filter ◽  
Initial Conditions ◽  
Specific Humidity ◽  
Error Statistics ◽  
Background Error ◽  
Fog Forecasting ◽  
Low Clouds ◽  
The Impact

Abstract Because poor visibility conditions have a considerable influence on airport traffic, a need exists for accurate and updated fog and low-cloud forecasts. Couche Brouillard Eau Liquide (COBEL)-Interactions between Soil, Biosphere, and Atmosphere (ISBA), a boundary layer 1D numerical model, has been developed for the very short-term forecast of fog and low clouds. This forecast system assimilates local observations to produce initial profiles of temperature and specific humidity. The initial conditions have a great impact on the skill of the forecast. In this work, the authors first estimated the background error statistics; they varied greatly with time, and cross correlations between temperature and humidity in the background were significant. This led to the implementation of an ensemble Kalman filter (EnKF) within COBEL-ISBA. The new assimilation system was evaluated with temperature and specific humidity scores, as well as in terms of its impact on the quality of fog forecasts. Simulated observations were used and focused on the modeling of the atmosphere before fog formation and also on the simulation of the life cycle of fog and low clouds. For both situations, the EnKF brought a significant improvement in the initial conditions and the forecasts. The forecast of the onset and burn-off times of fogs was also improved. The EnKF was also tested with real observations and gave good results. The size of the ensemble did not have much impact when simulated observations were used, thanks to an adaptive covariance inflation algorithm, but the impact was greater when real observations were used.

Observational Error Estimation of FORMOSAT-3/COSMIC GPS Radio Occultation Data

2011 ◽  
Vol 139 (3) ◽  
pp. 853-865 ◽  
Author(s):  
Shu-Ya Chen ◽  
Ching-Yuang Huang ◽  
Ying-Hwa Kuo ◽  
Sergey Sokolovskiy
Keyword(s):  
Radio Occultation ◽  
Specific Humidity ◽  
Forecast Errors ◽  
Maximum Magnitude ◽  
Observational Error ◽  
Error Statistics ◽  
Gps Radio Occultation ◽  
Horizontal Gradients ◽  
Excess Phase ◽  
Observational Errors

Abstract The Global Positioning System (GPS) radio occultation (RO) technique is becoming a robust global observing system. GPS RO refractivity is typically modeled at the ray perigee point by a “local refractivity operator” in a data assimilation system. Such modeling does not take into account the horizontal gradients that affect the GPS RO refractivity. A new observable (linear excess phase), defined as an integral of the refractivity along some fixed ray path within the model domain, has been developed in earlier studies to account for the effect of horizontal gradients. In this study, the error statistics of both observables (refractivity and linear excess phase) are estimated using the GPS RO data from the Formosa Satellite 3–Constellation Observing System for Meteorology, Ionosphere and Climate (FORMOSAT-3/COSMIC) mission. The National Meteorological Center (NMC) method, which is based on lagged forecast differences, is applied for evaluation of the model forecast errors that are used for estimation of the GPS RO observational errors. Also used are Weather Research and Forecasting (WRF) model forecasts in the East Asia region at 45-km resolution for one winter month (mid-January to mid-February) and one summer month (mid-August to mid-September) in 2007. Fractional standard deviations of the observational errors of refractivity and linear excess phase both show an approximately linear decrease with height in the troposphere and a slight increase above the tropopause; their maximum magnitude is about 2.2% (2.5%) for refractivity and 1.1% (1.3%) for linear excess phase in the lowest 2 km for the winter (summer) month. An increase of both fractional observational errors near the surface in the summer month is attributed mainly to a larger amount of water vapor. The results indicate that the fractional observational error of refractivity is about twice as large as that of linear excess phase, regardless of season. The observational errors of both linear excess phase and refractivity are much less latitude dependent for summer than for winter. This difference is attributed to larger latitudinal variations of the specific humidity in winter.

scholarly journals Assessing the Impact of Simulated COSMIC GPS Radio Occultation Data on Weather Analysis over the Antarctic: A Case Study

2006 ◽  
Vol 134 (11) ◽  
pp. 3283-3296 ◽  
Author(s):  
L. Cucurull ◽  
Y-H. Kuo ◽  
D. Barker ◽  
S. R. H. Rizvi
Keyword(s):  
Radio Occultation ◽  
Time Window ◽  
Mesoscale Model ◽  
Specific Humidity ◽  
Horizontal Wind ◽  
Wind Component ◽  
Gps Radio Occultation ◽  
Potential Impact ◽  
The Antarctic ◽  
The Impact

Abstract The Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission was launched in April 2006. As part of its mission, COSMIC will provide approximately 2500–3000 global positioning system (GPS) radio occultation (RO) soundings per day distributed uniformly around the globe. In this study, a series of sensitivity experiments are conducted to assess the potential impact of COSMIC GPS RO data on the regional weather analysis over the Antarctic. Soundings of refractivity are assimilated into the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model using its three-dimensional variational data assimilation system. First, the sensitivity of the analysis to the background error statistics and balance constraints is analyzed. Then the effects of the data distribution and the observational error of the simulated refractivity observations are examined. In this study, the simulated soundings are based on a realistic set of orbit parameters of the COSMIC constellation. Analysis of the assimilation results indicates the significant potential impact of COSMIC data on regional analyses over the Antarctic. In the one case studied here, the root-mean-square differences between the background and observed values are reduced by 12% in the horizontal wind component, 17% in the temperature variable, 8% in the specific humidity, and 22% in the pressure field when COSMIC GPS RO data are assimilated into the system by using a 6-h assimilation time window. These preliminary results suggest that COSMIC GPS RO data can have a significant impact on operational numerical weather analysis in the Antarctic.

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