What Experiment Did We Just Do? Counterfactual Error Statistics And Uncertainties About The Reference Class*

2002 ◽  
Vol 69 (2) ◽  
pp. 279-299 ◽  
Author(s):  
Kent W. Staley
Keyword(s):  
Error Statistics ◽  
Reference Class

scholarly journals Composite Aerosol Optical Depth Mapping over Northeast Asia from GEO-LEO Satellite Observations

2021 ◽  
Vol 13 (6) ◽  
pp. 1096
Author(s):  
Soi Ahn ◽  
Sung-Rae Chung ◽  
Hyun-Jong Oh ◽  
Chu-Yong Chung
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Northeast Asia ◽  
Low Earth Orbit ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Radiometric Calibration ◽  
Climate Data ◽  
Error Statistics ◽  
Spatiotemporal Resolution

This study aimed to generate a near real time composite of aerosol optical depth (AOD) to improve predictive model ability and provide current conditions of aerosol spatial distribution and transportation across Northeast Asia. AOD, a proxy for aerosol loading, is estimated remotely by various spaceborne imaging sensors capturing visible and infrared spectra. Nevertheless, differences in satellite-based retrieval algorithms, spatiotemporal resolution, sampling, radiometric calibration, and cloud-screening procedures create significant variability among AOD products. Satellite products, however, can be complementary in terms of their accuracy and spatiotemporal comprehensiveness. Thus, composite AOD products were derived for Northeast Asia based on data from four sensors: Advanced Himawari Imager (AHI), Geostationary Ocean Color Imager (GOCI), Moderate Infrared Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS). Cumulative distribution functions were employed to estimate error statistics using measurements from the Aerosol Robotic Network (AERONET). In order to apply the AERONET point-specific error, coefficients of each satellite were calculated using inverse distance weighting. Finally, the root mean square error (RMSE) for each satellite AOD product was calculated based on the inverse composite weighting (ICW). Hourly AOD composites were generated (00:00–09:00 UTC, 2017) using the regression equation derived from the comparison of the composite AOD error statistics to AERONET measurements, and the results showed that the correlation coefficient and RMSE values of composite were close to those of the low earth orbit satellite products (MODIS and VIIRS). The methodology and the resulting dataset derived here are relevant for the demonstrated successful merging of multi-sensor retrievals to produce long-term satellite-based climate data records.

Optimization of Signal Processing Applications Using Parameterized Error Models for Approximate Adders

2021 ◽  
Vol 20 (2) ◽  
pp. 1-25
Author(s):  
Celia Dharmaraj ◽  
Vinita Vasudevan ◽  
Nitin Chandrachoodan
Keyword(s):  
Signal Processing ◽  
Circuit Design ◽  
Computational Efficiency ◽  
Noise Power ◽  
Mean Square ◽  
Error Statistics ◽  
Error Models ◽  
Optimization Framework ◽  
Accuracy Constraint ◽  
Multi Level

Approximate circuit design has gained significance in recent years targeting error-tolerant applications. In the literature, there have been several attempts at optimizing the number of approximate bits of each approximate adder in a system for a given accuracy constraint. For computational efficiency, the error models used in these routines are simple expressions obtained using regression or by assuming inputs or the error is uniformly distributed. In this article, we first demonstrate that for many approximate adders, these assumptions lead to an inaccurate prediction of error statistics for multi-level circuits. We show that mean error and mean square error can be computed accurately if static probabilities of adders at all stages are taken into account. Therefore, in a system with a certain type of approximate adder, any optimization framework needs to take into account not just the functionality of the adder but also its position in the circuit, functionality of its parents, and the number of approximate bits in the parent blocks. We propose a method to derive parameterized error models for various types of approximate adders. We incorporate these models within an optimization framework and demonstrate that the noise power is computed accurately.

Improved Accuracy Models For Hourly Diffuse Solar Radiation

2005 ◽  
Vol 128 (1) ◽  
pp. 104-117 ◽  
Author(s):  
T. Muneer ◽  
S. Munawwar
Keyword(s):  
Solar Radiation ◽  
Regression Models ◽  
Global Radiation ◽  
Diffuse Radiation ◽  
Evaluation Procedure ◽  
Accuracy Score ◽  
Error Statistics ◽  
Diffuse Solar Radiation ◽  
Energy Applications ◽  
Extensive Evaluation

Solar energy applications require readily available, site-oriented, and long-term solar data. However, the frequent unavailability of diffuse irradiation, in contrast to its need, has led to the evolution of various regression models to predict it from the more commonly available data. Estimating the diffuse component from global radiation is one such technique. The present work focuses on improvement in the accuracy of the models for predicting horizontal diffuse irradiation using hourly solar radiation database from nine sites across the globe. The influence of sunshine fraction, cloud cover, and air mass on estimation of diffuse radiation is investigated. Inclusion of these along with hourly clearness index, leads to the development of a series of models for each site. Estimated values of hourly diffuse radiation are compared with measured values in terms of error statistics and indicators like, R2, mean bias deviation, root mean square deviation, skewness, and kurtosis. A new method called “the accuracy score system” is devised to assess the effect on accuracy with subsequent addition of each parameter and increase in complexity of equation. After an extensive evaluation procedure, extricate but adequate models are recommended as optimum for each of the nine sites. These models were found to be site dependent but the model types were fairly consistent for neighboring stations or locations with similar climates. Also, this study reveals a significant improvement from the conventional k-kt regression models to the presently proposed models.

scholarly journals Multivariate Error Covariance Estimates by Monte Carlo Simulation for Assimilation Studies in the Pacific Ocean

2005 ◽  
Vol 133 (8) ◽  
pp. 2310-2334 ◽  
Author(s):  
Anna Borovikov ◽  
Michele M. Rienecker ◽  
Christian L. Keppenne ◽  
Gregory C. Johnson
Keyword(s):  
Monte Carlo ◽  
Data Assimilation ◽  
Forecast Error ◽  
Independent Set ◽  
Velocity Fields ◽  
Optimal Interpolation ◽  
Model Errors ◽  
Error Statistics ◽  
Practical Applications ◽  
Monte Carlo Techniques

Abstract One of the most difficult aspects of ocean-state estimation is the prescription of the model forecast error covariances. The paucity of ocean observations limits our ability to estimate the covariance structures from model–observation differences. In most practical applications, simple covariances are usually prescribed. Rarely are cross covariances between different model variables used. Here a comparison is made between a univariate optimal interpolation (UOI) scheme and a multivariate OI algorithm (MvOI) in the assimilation of ocean temperature profiles. In the UOI case only temperature is updated using a Gaussian covariance function. In the MvOI, salinity, zonal, and meridional velocities as well as temperature are updated using an empirically estimated multivariate covariance matrix. Earlier studies have shown that a univariate OI has a detrimental effect on the salinity and velocity fields of the model. Apparently, in a sequential framework it is important to analyze temperature and salinity together. For the MvOI an estimate of the forecast error statistics is made by Monte Carlo techniques from an ensemble of model forecasts. An important advantage of using an ensemble of ocean states is that it provides a natural way to estimate cross covariances between the fields of different physical variables constituting the model-state vector, at the same time incorporating the model’s dynamical and thermodynamical constraints as well as the effects of physical boundaries. Only temperature observations from the Tropical Atmosphere–Ocean array have been assimilated in this study. To investigate the efficacy of the multivariate scheme, two data assimilation experiments are validated with a large independent set of recently published subsurface observations of salinity, zonal velocity, and temperature. For reference, a control run with no data assimilation is used to check how the data assimilation affects systematic model errors. While the performance of the UOI and MvOI is similar with respect to the temperature field, the salinity and velocity fields are greatly improved when the multivariate correction is used, as is evident from the analyses of the rms differences between these fields and independent observations. The MvOI assimilation is found to improve upon the control run in generating water masses with properties close to the observed, while the UOI fails to maintain the temperature and salinity structure.

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 An alternative way to evaluate chemistry-transport model variability

2017 ◽  
Vol 10 (3) ◽  
pp. 1199-1208 ◽  
Author(s):  
Laurent Menut ◽  
Sylvain Mailler ◽  
Bertrand Bessagnet ◽  
Guillaume Siour ◽  
Augustin Colette ◽  
...  
Keyword(s):  
Transport Model ◽  
Model Performance ◽  
Error Statistics ◽  
Chemistry Transport Model ◽  
Temporal Correlations ◽  
Time Period ◽  
The Difference ◽  
Model Variability ◽  
Spatial Locations ◽  
Statistical Indicators

Abstract. A simple and complementary model evaluation technique for regional chemistry transport is discussed. The methodology is based on the concept that we can learn about model performance by comparing the simulation results with observational data available for time periods other than the period originally targeted. First, the statistical indicators selected in this study (spatial and temporal correlations) are computed for a given time period, using colocated observation and simulation data in time and space. Second, the same indicators are used to calculate scores for several other years while conserving the spatial locations and Julian days of the year. The difference between the results provides useful insights on the model capability to reproduce the observed day-to-day and spatial variability. In order to synthesize the large amount of results, a new indicator is proposed, designed to compare several error statistics between all the years of validation and to quantify whether the period and area being studied were well captured by the model for the correct reasons.

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