scholarly journals Why Are Radar Data so Difficult to Assimilate Skillfully?

2020 ◽  
Vol 148 (7) ◽  
pp. 2819-2836 ◽  
Author(s):  
Frédéric Fabry ◽  
Véronique Meunier
Keyword(s):  
Data Assimilation ◽  
Radar Data ◽  
State Variables ◽  
New Information ◽  
Common Practices ◽  
Radar Measurements ◽  
The Common ◽  
Statistical Relationships ◽  
Available Information ◽  
Traditional Approaches

Abstract Although radar is our most useful tool for monitoring severe weather, the benefits of assimilating its data are often short lived. To understand why, we documented the assimilation requirements, the data characteristics, and the common practices that could hinder optimum data assimilation by traditional approaches. Within storms, radars provide dense measurements of a few highly variable storm outcomes (precipitation and wind) in atmospherically unstable conditions. However, statistical relationships between errors of observed and unobserved quantities often become nonlinear because the errors in these areas tend to become large rapidly. Beyond precipitating areas lie large regions for which radars provide limited new information, yet whose properties will soon shape the outcome of future storms. For those areas, any innovation must consequently be projected from sometimes distant precipitating areas. Thus, radar data assimilation must contend with a double need at odds with many traditional assimilation implementations: correcting in-storm properties with complex errors while projecting information at unusually far distances outside precipitating areas. To further complicate the issue, other data properties and practices, such as assimilating reflectivity in logarithmic units, are not optimal to correct all state variables. Therefore, many characteristics of radar measurements and common practices of their assimilation are incompatible with necessary conditions for successful data assimilation. Facing these dataset-specific challenges may force us to consider new approaches that use the available information differently.

scholarly journals Development of a Hybrid En3DVar Data Assimilation System and Comparisons with 3DVar and EnKF for Radar Data Assimilation with Observing System Simulation Experiments

2018 ◽  
Vol 146 (1) ◽  
pp. 175-198 ◽  
Author(s):  
Rong Kong ◽  
Ming Xue ◽  
Chengsi Liu
Keyword(s):  
Data Assimilation ◽  
System Simulation ◽  
Radar Data ◽  
Mixing Ratio ◽  
State Variables ◽  
Ensemble Size ◽  
Simulation Experiments ◽  
Background Error ◽  
Variational Minimization ◽  
Water Vapor Mixing Ratio

Abstract A hybrid ensemble–3DVar (En3DVar) system is developed and compared with 3DVar, EnKF, “deterministic forecast” EnKF (DfEnKF), and pure En3DVar for assimilating radar data through perfect-model observing system simulation experiments (OSSEs). DfEnKF uses a deterministic forecast as the background and is therefore parallel to pure En3DVar. Different results are found between DfEnKF and pure En3DVar: 1) the serial versus global nature and 2) the variational minimization versus direct filter updating nature of the two algorithms are identified as the main causes for the differences. For 3DVar (EnKF/DfEnKF and En3DVar), optimal decorrelation scales (localization radii) for static (ensemble) background error covariances are obtained and used in hybrid En3DVar. The sensitivity of hybrid En3DVar to covariance weights and ensemble size is examined. On average, when ensemble size is 20 or larger, a 5%–10% static covariance gives the best results, while for smaller ensembles, more static covariance is beneficial. Using an ensemble size of 40, EnKF and DfEnKF perform similarly, and both are better than pure and hybrid En3DVar overall. Using 5% static error covariance, hybrid En3DVar outperforms pure En3DVar for most state variables but underperforms for hydrometeor variables, and the improvement (degradation) is most notable for water vapor mixing ratio qυ (snow mixing ratio qs). Overall, EnKF/DfEnKF performs the best, 3DVar performs the worst, and static covariance only helps slightly via hybrid En3DVar.

scholarly journals Hybridizing sequential and variational data assimilation for robust high-resolution hydrologic forecasting

2016 ◽  
Author(s):  
Felipe Hernández ◽  
Xu Liang
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Variational Data Assimilation ◽  
Sequential Data ◽  
State Variables ◽  
Hydrologic Modelling ◽  
Multi Objective ◽  
High Resolution Models ◽  
Computational Resources ◽  
Traditional Approaches

Abstract. There are two main frameworks for the estimation of initial states in geophysical models for real-time and forecasting applications: sequential data assimilation and variational data assimilation. However, modern high-resolution models offer challenges, both in terms of indeterminacy and computational requirements, which render most traditional methods insufficient. In this article we introduce a hybrid algorithm called OPTIMISTS which combines advantageous features from both of these data assimilation perspectives. These features are integrated with a multi-objective approach for selecting ensemble members to create a probabilistic estimate of the state variables, which promotes the reduction of observational errors as well as the maintenance of the dynamic consistency of states. Additionally, we propose simplified computations as alternatives aimed at reducing memory and processor requirements. OPTIMISTS was tested on two models of real watersheds, one with over 1,000 variables and the second with over 30,000, on two distributed hydrologic modelling engines: VIC and the DHSVM. Our tests, consisting of assimilating streamflow observations, allowed determining which features of the traditional approaches lead to more accurate forecasts while at the same time making an efficient use of the available computational resources. The results also demonstrated the benefits of the coupled probabilistic/multi-objective approach, which proved instrumental in reducing the harmful effects of overfitting – especially on the model with higher dimensionality.

scholarly journals Improving the Model Convective Storm Quantitative Precipitation Nowcasting by Assimilating State Variables Retrieved from Multiple-Doppler Radar Observations

2014 ◽  
Vol 142 (11) ◽  
pp. 4017-4035 ◽  
Author(s):  
Yu-Chieng Liou ◽  
Jian-Luen Chiou ◽  
Wei-Hao Chen ◽  
Hsin-Yu Yu
Keyword(s):  
Data Assimilation ◽  
Doppler Radar ◽  
Scale Up ◽  
Three Dimensional ◽  
Radar Data ◽  
State Variables ◽  
Weather System ◽  
Additional Information ◽  
Convective Scale ◽  
Fine Resolution

Abstract This research combines an advanced multiple-Doppler radar synthesis technique with the thermodynamic retrieval method, originally proposed by Gal-Chen, and a moisture/temperature adjustment scheme, and formulates a sequential procedure. The focus is on applying this procedure to improve the model quantitative precipitation nowcasting (QPN) skill in the convective scale up to 3 hours. A series of (observing system simulation experiment) OSSE-type tests and a real case study are conducted to investigate the performance of this algorithm under different conditions. It is shown that by using the retrieved three-dimensional wind, thermodynamic, and microphysical parameters to reinitialize a fine-resolution numerical model, its QPN skill can be significantly improved. Since the Gal-Chen method requires the horizontal average properties of the weather system at each altitude, utilization of in situ radiosonde(s) to obtain this additional information for the retrieval is tested. When sounding data are not available, it is demonstrated that using the model results to replace the role played by observing devices is also a feasible choice. The moisture field is obtained through a simple, but effective, adjusting scheme and is found to be beneficial to the rainfall forecast within the first hour after the reinitialization of the model. Since this algorithm retrieves the unobserved state variables instantaneously from the wind measurements and directly uses them to reinitialize the model, fewer radar data and a shorter model spinup time are needed to correct the rainfall forecasts, in comparison with other data assimilation techniques such as four-dimensional variational data assimilation (4DVAR) or ensemble Kalman filter (EnKF) methods.

KOMIK DI LINGKARAN INDUSTRI MUSIK

DeKaVe ◽  
2013 ◽  
Vol 1 (2) ◽  
Author(s):  
Terra Bajraghosa
Keyword(s):  
Comic Books ◽  
Music Industry ◽  
Comic Book ◽  
Narrative Approach ◽  
Visual Narrative ◽  
Common Practices ◽  
Visual Style ◽  
Song Lyrics ◽  
The Common

Comic ges. Based on comprehention as a narrative media, comics in Indonesia are oftenly compared to bas-reliefs on Borobudur temple and Wayang Beber.. From many kind of stories Indonesian comic books recently offered, with the developed visual wrapping, some comics steal attentions by its unique themes. These comic books are created because of the inspiration and relation to music industry. These comic books couldn't be seen from the visual style alone, as they were created in many visual genres, but they could be seen from their relations to music industry, whether the mainstream or indie ones. These comic books are published together with the music CDs, telling fictional stories from factual bands or musicians, telling a band's factual stories, or created by one of the band members. To understand modes of creation of these music industry-related-comic books, visual narrative approach will be applied. Through visual narrative approach, the band members' or musician's necessity for telling stories via comics, beside the common practices via music and song lyrics, will be observed.Keywords : Comic book, music industry, visual narrative

Children change their answers in response to neutral follow-up questions by a knowledgeable asker

2019 ◽  
Author(s):  
Elizabeth Bonawitz ◽  
Patrick Shafto ◽  
Yue Yu ◽  
Sophie Elizabeth Colby Bridgers ◽  
Aaron Gonzalez
Keyword(s):  
Social Learning ◽  
Belief Revision ◽  
Correct Answer ◽  
New Information ◽  
Common Practices ◽  
Parent Child

Burgeoning evidence suggests that when children observe data, they use knowledge of the demonstrator’s intent to augment learning. We propose that the effects of social learning may go beyond cases where children observe data, to cases where they receive no new information at all. We present a model of how simply asking a question a second time may lead to belief revision, when the questioner is expected to know the correct answer. We provide an analysis of the CHILDES corpus to show that these neutral follow-up questions are used in parent-child conversations. We then present three experiments investigating 4- and 5-year-old children’s reactions to neutral follow-up questions posed by ignorant or knowledgeable questioners. Children were more likely to change their answers in response to a neutral follow-up question from a knowledgeable questioner than an ignorant one. We discuss the implications of these results in the context of common practices in legal, educational, and experimental psychological settings.

Impact of radar data assimilation on simulations of precipitable water with the Harmonie model: A case study over Cyprus

2021 ◽  
Vol 253 ◽  
pp. 105473
Author(s):  
Serguei Ivanov ◽  
Silas Michaelides ◽  
Igor Ruban ◽  
Demetris Charalambous ◽  
Filippos Tymvios
Keyword(s):  
Data Assimilation ◽  
Precipitable Water ◽  
Radar Data ◽  
Radar Data Assimilation

The trees and the forest: Investigating variability surrounding an aggregate result

2021 ◽  
Vol 31 (3) ◽  
pp. 399-404
Author(s):  
Paul De Boeck ◽  
Michael L. DeKay ◽  
L. Robert Gore ◽  
Minjeong Jeon
Keyword(s):  
Control Theory ◽  
Paradigm Shift ◽  
Replication Studies ◽  
The Common ◽  
Psychological Meaningfulness ◽  
Traditional Approaches ◽  
Exclusive Focus ◽  
Psychological Studies

We agree with Arocha that the common and exclusive focus on aggregate results of psychological studies creates problems. While a paradigm shift toward idiographic approaches or control theory might help, we argue that traditional approaches can accomplish more if measures of variability are taken seriously. We discuss three kinds of studies: within-person treatment studies, questionnaire-based studies, and replication studies. For each of these, we suggest ways to improve psychological meaningfulness by investigating variability surrounding aggregate results, without ending up in an either–or choice between aggregate results and separate, individual results.

scholarly journals Variables in Globular Clusters, the Common and the Rare

1973 ◽  
Vol 21 ◽  
pp. 3-7
Author(s):  
Helen Sawyer Hogg
Keyword(s):  
Globular Clusters ◽  
New Information ◽  
The Subject ◽  
The Common

The title of this talk is really just a different phrasing from one I have used at several IAU meetings on the subject of numbers and kinds of variables in globular clusters. To furnish this material, I have finished the Third Catalogue of Variables in Globular Clusters. Since many of you are coming to this Colloquium with new information, the Catalogue is in draft form with a request that corrections and additions be given me by October 2, after which the draft will go to the printer.The First Catalogue of Variables in Globular Clusters was published at this observatory in 1939 and the Second Catalogue in 1955. In 1966 appeared the excellent Catalogue of Variables South of Declination—29° by Fourcade, Laborde and Albarracin, with splendid large prints of identification charts.

scholarly journals Predictability of Deep Convection in Idealized and Operational Forecasts: Effects of Radar Data Assimilation, Orography, and Synoptic Weather Regime

2019 ◽  
Vol 148 (1) ◽  
pp. 63-81 ◽  
Author(s):  
Kevin Bachmann ◽  
Christian Keil ◽  
George C. Craig ◽  
Martin Weissmann ◽  
Christian A. Welzbacher
Keyword(s):  
Data Assimilation ◽  
Deep Convection ◽  
Radar Data ◽  
Ensemble Prediction ◽  
Small Scale ◽  
Model Errors ◽  
Ensemble Prediction System ◽  
Beneficial Impact ◽  
The Impact ◽  
Radar Data Assimilation

Abstract We investigate the practical predictability limits of deep convection in a state-of-the-art, high-resolution, limited-area ensemble prediction system. A combination of sophisticated predictability measures, namely, believable and decorrelation scale, are applied to determine the predictable scales of short-term forecasts in a hierarchy of model configurations. First, we consider an idealized perfect model setup that includes both small-scale and synoptic-scale perturbations. We find increased predictability in the presence of orography and a strongly beneficial impact of radar data assimilation, which extends the forecast horizon by up to 6 h. Second, we examine realistic COSMO-KENDA simulations, including assimilation of radar and conventional data and a representation of model errors, for a convectively active two-week summer period over Germany. The results confirm increased predictability in orographic regions. We find that both latent heat nudging and ensemble Kalman filter assimilation of radar data lead to increased forecast skill, but the impact is smaller than in the idealized experiments. This highlights the need to assimilate spatially and temporally dense data, but also indicates room for further improvement. Finally, the examination of operational COSMO-DE-EPS ensemble forecasts for three summer periods confirms the beneficial impact of orography in a statistical sense and also reveals increased predictability in weather regimes controlled by synoptic forcing, as defined by the convective adjustment time scale.

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