Balance properties of the short-range forecast errors in the ECMWF 4D-Var ensemble

2012 ◽  
Vol 139 (674) ◽  
pp. 1229-1238 ◽  
Author(s):  
N. Žagar ◽  
L. Isaksen ◽  
D. Tan ◽  
J. Tribbia
Keyword(s):  
Short Range ◽  
Forecast Errors

scholarly journals Storm-Scale Data Assimilation and Ensemble Forecasts for the 27 April 2011 Severe Weather Outbreak in Alabama

2015 ◽  
Vol 143 (8) ◽  
pp. 3044-3066 ◽  
Author(s):  
Nusrat Yussouf ◽  
David C. Dowell ◽  
Louis J. Wicker ◽  
Kent H. Knopfmeier ◽  
Dustan M. Wheatley
Keyword(s):  
Short Range ◽  
Type System ◽  
Severe Weather ◽  
Forecast Errors ◽  
Model Errors ◽  
Ensemble Forecasts ◽  
Low Level ◽  
Design Storm ◽  
Probabilistic Forecasts ◽  
Arw Model

Abstract As part of NOAA’s Warn-on-Forecast (WoF) initiative, a multiscale ensemble-based assimilation and prediction system is developed using the WRF-ARW model and DART assimilation software. To evaluate the capabilities of the system, retrospective short-range probabilistic storm-scale (convection allowing) ensemble analyses and forecasts are produced for the 27 April 2011 Alabama severe weather outbreak. Results indicate that the storm-scale ensembles are able to analyze the observed storms with strong low-level rotation at approximately the correct locations and to retain the supercell structures during the 0–1-h forecasts with reasonable accuracy. The system predicts the low-level mesocyclones of significant isolated tornadic supercells that align well with the locations of radar-derived rotation. For cases with multiple interacting storms in close proximity, the system tends to produce more variability in mesocyclone forecasts from one initialization time to the next until the observations show the dominance of one of the cells. The short-range ensemble probabilistic forecasts obtained from this continuous 5-min storm-scale 6-h-long update system demonstrate the potential of a frequently updated, high-resolution NWP system that could be used to extend severe weather warning lead times. This study also demonstrates the challenges associated with developing a WoF-type system. The results motivate future work to reduce model errors associated with storm motion and spurious cells, and to design storm-scale ensembles that better represent typical 1-h forecast errors.

Diagnosis of the Initial and Forecast Errors in the Numerical Simulation of the Rapid Intensification of Hurricane Emily (2005)

2009 ◽  
Vol 24 (5) ◽  
pp. 1236-1251 ◽  
Author(s):  
Zhaoxia Pu ◽  
Xuanli Li ◽  
Edward J. Zipser
Keyword(s):  
Numerical Simulation ◽  
Short Range ◽  
Inner Core ◽  
Initial Conditions ◽  
Forecast Errors ◽  
Rapid Intensification ◽  
Convective Structures ◽  
Level Data ◽  
Arw Model ◽  
Flight Level

Abstract A diagnostic study is conducted to examine the initial and forecast errors in a short-range numerical simulation of Hurricane Emily’s (2005) early rapid intensification. The initial conditions and the simulated hurricane vortices using high-resolution grids (1 and 3 km), generated from the Advanced Research version of the Weather Research and Forecasting (ARW) model and its three-dimensional variational data assimilation (3DVAR) systems, are compared with the flight-level data acquired from the U.S. Air Force C-130J aircraft data. Numerical simulation results show that the model fails at predicting the actual rapid intensification of the hurricane, although the initial intensity of the vortex matches the observed intensity. Comparing the model results with aircraft flight-level data, unrealistic thermal and convective structures of the storm eyewall are found in the initial conditions. In addition, the simulated eyewall does not contract rapidly enough during the model simulation. Increasing the model’s horizontal resolution from 3 to 1 km can help the model to produce a deeper storm and also a more realistic eye structure. However, even at 1 km the model is still not able to fully resolve the inner-core structures. To provide additional insight, a set of mesoscale reanalyses is generated through the assimilation of available satellite and aircraft dropsonde data into the ARW model throughout the whole simulation period at a 6-h interval. It is found that the short-range numerical simulation of the hurricane has been greatly improved by the mesoscale reanalysis; the data assimilation helps the model to reproduce stronger wind, thermal, and convective structures of the storm, and a more realistic eyewall contraction and eye structure. Results from this study suggest that a more accurate representation of the hurricane vortex, especially the inner-core structures in the initial conditions, is necessary for a more accurate forecast of hurricane rapid intensification.

scholarly journals The Characteristics of Key Analysis Errors. Part II: The Importance of the PV Corrections and the Impact of Balance

2007 ◽  
Vol 135 (2) ◽  
pp. 267-280 ◽  
Author(s):  
Jean-François Caron ◽  
M. K. Yau ◽  
Stéphane Laroche ◽  
Peter Zwack
Keyword(s):  
Short Range ◽  
Vertical Motion ◽  
Forecast Errors ◽  
Divergent Part ◽  
Essential Information ◽  
Original Analysis ◽  
Analysis Error ◽  
Motion Corrections ◽  
The Impact ◽  
Analysis Errors

Abstract This study examines a few approaches to isolate the balanced component of the initial corrections from the Canadian Meteorological Centre energy-norm-based key analysis error algorithm, in an attempt to capture the part of the key analysis errors responsible for short-range forecast errors. The best results were obtained with the nonlinear balance potential vorticity (PV) inversion technique. It was shown that the PV component of the initial corrections contains the essential information for reducing short-range forecast errors. The remaining imbalance part of the initial corrections does not grow in time and does not contribute to the improvement of the forecast. The removal of the imbalance part of the initial corrections makes the corrected analysis slightly closer to the observations, but remains systematically farther away as compared with the original analysis. Thus the balanced part of the key analysis errors cannot justifiably be associated to analysis errors. A methodology to balance the divergent part of the initial corrections, which reduces significantly the spinup in the vertical motion corrections, is also presented. Finally, in light of the results presented in this paper, some recommendations to improve the key analysis error algorithm are proposed.

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