Ensemble Synoptic Analysis
Gregory J. Hakim,Ryan D. Torn +1 more
TL;DR: This work proposes a new approach, ensemble synoptic analysis, which exploits the information contained in probabilistic samples of analyses at one or more instants in time, and exploits the relationships between all locations and all variables at that instant in time.
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Abstract: Synoptic and mesoscale meteorology underwent a revolution in the 1940s and 1950s with the widespread deployment of novel weather observations, such as the radiosonde network and the advent of weather radar. These observations provoked a rapid increase in our understanding of the structure and dynamics of the atmosphere by pioneering analysts such as Fred Sanders. The authors argue that we may be approaching an analogous revolution in our ability to study the structure and dynamics of atmospheric phenomena with the advent of probabilistic objective analyses. These probabilistic analyses provide not only best estimates of the state of the atmosphere (e.g., the expected value) and the uncertainty about this state (e.g., the variance), but also the relationships between all locations and all variables at that instant in time. Up until now, these relationships have been determined by sampling in time by, for example, case studies, composites, and time-series analysis. Here the authors propose a new approach, ensemble synoptic analysis, which exploits the information contained in probabilistic samples of analyses at one or more instants in time.
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Citations
Comparing Adjoint- and Ensemble-Sensitivity Analysis with Applications to Observation Targeting
Brian C. Ancell,Gregory J. Hakim +1 more
TL;DR: In this paper, the sensitivity of numerical weather forecasts to small changes in initial conditions is estimated using ensemble samples of analysis and forecast errors, defined by linear regression of analysis errors onto a given forecast metric.
Ensemble-Based Sensitivity Analysis
Ryan D. Torn,Gregory J. Hakim +1 more
TL;DR: In this paper, the effect of observations on the forecast metric is quantified by changes in the metric mean and variance for a single observation, expressions for these changes involve a product of scalar quantities which can be rapidly evaluated for large numbers of observations.
Tests of an Ensemble Kalman Filter for Mesoscale and Regional-Scale Data Assimilation. Part IV: Comparison with 3DVAR in a Month-Long Experiment
Zhiyong Meng,Fuqing Zhang +1 more
TL;DR: In this paper, an ensemble Kalman filter (EnKF) was demonstrated to be promising for mesoscale and regional-scale data assimilation in increasingly realistic environments, and the authors extended the single-case real-data experiments over a period of 1 month to examine the long-term performance and comparison of both methods at the regional scales.
Sensitivity of Midlatitude Storm Intensification to Perturbations in the Sea Surface Temperature near the Gulf Stream
TL;DR: In this article, a storm that intensified as it transited northward across the Gulf Stream is simulated multiple times using different SST boundary conditions, and the storm response to the SST perturbations is driven by the latent heat release in the storm warm conveyor belt (WCB).
108
Performance of a Mesoscale Ensemble Kalman Filter (EnKF) during the NOAA High-Resolution Hurricane Test
TL;DR: An ensemble Kalman filter (EnKF) combined with the Advanced Research Weather Research and Forecasting model (ARW-WRF) on a 36-km Atlantic basin domain is cycled over six different time periods that include the 10 tropical cyclones (TCs) selected for the NOAA High-Resolution Hurricane (HRH) test as discussed by the authors.
103
References
Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics
TL;DR: In this article, a new sequential data assimilation method is proposed based on Monte Carlo methods, a better alternative than solving the traditional and computationally extremely demanding approximate error covariance equation used in the extended Kalman filter.
An introduction to dynamic meteorology
James R. Holton
- 25 Feb 2004
TL;DR: The instructor's manual to a work which introduces the fundamental principles of meteorology, explaining storm dynamics and the dynamics of climate and its global implications is described in this paper, where the authors present a detailed discussion of the relationship between meteorology and climate.
4.8K
An introduction to dynamic meteorology
James R. Holton
- 01 Jan 1992
TL;DR: The instructor's manual to a work which introduces the fundamental principles of meteorology, explaining storm dynamics and the dynamics of climate and its global implications is described in this article, where the authors present a detailed discussion of the relationship between meteorology and climate.
3.7K
The Step-Mountain Eta Coordinate Model: Further Developments of the Convection, Viscous Sublayer, and Turbulence Closure Schemes
TL;DR: In this article, a major revision of the Betts and Miller convection scheme was made, a new marine viscous sublayer scheme was designed, and the Mellor-Yamada planetary boundary layer (PBL) formulation was retuned.
•Book
Atmospheric Modeling, Data Assimilation and Predictability
Eugenia Kalnay
- 01 Nov 2002
TL;DR: A comprehensive text and reference work on numerical weather prediction, first published in 2002, covers not only methods for numerical modeling, but also the important related areas of data assimilation and predictability.