Achieving Superior Tropical Cyclone Intensity Forecasts by Improving the Assimilation of High-Resolution Satellite Data into Mesoscale Prediction Models

2011 ◽  
Author(s):  
Christopher Velden ◽  
Sharanya J. Majumdar ◽  
Jun Li ◽  
Hui Liu ◽  
James D. Doyle ◽  
...  
Keyword(s):  
High Resolution ◽  
Tropical Cyclone ◽  
Satellite Data ◽  
Prediction Models ◽  
Tropical Cyclone Intensity ◽  
Cyclone Intensity ◽  
Tropical Cyclone Intensity Forecasts

Achieving Superior Tropical Cyclone Intensity Forecasts by Improving the Assimilation of High-Resolution Satellite Data into Mesoscale Prediction Models

2013 ◽  
Author(s):  
Christopher Velden ◽  
Sharanya J. Majumdar ◽  
Jun Li ◽  
Hui Liu ◽  
James D. Doyle ◽  
...  
Keyword(s):  
High Resolution ◽  
Tropical Cyclone ◽  
Satellite Data ◽  
Prediction Models ◽  
Tropical Cyclone Intensity ◽  
Cyclone Intensity ◽  
Tropical Cyclone Intensity Forecasts

scholarly journals Achieving Superior Tropical Cyclone Intensity Forecasts by Improving the Assimilation of High-Resolution Satellite Data into Mesoscale Prediction Models

2012 ◽  
Author(s):  
Christopher Velden ◽  
Sharanya J. Majumdar ◽  
Jun Li ◽  
Hui Liu ◽  
James D. Doyle ◽  
...  
Keyword(s):  
High Resolution ◽  
Tropical Cyclone ◽  
Satellite Data ◽  
Prediction Models ◽  
Tropical Cyclone Intensity ◽  
Cyclone Intensity ◽  
Tropical Cyclone Intensity Forecasts

Trend Analysis with a New Global Record of Tropical Cyclone Intensity

2013 ◽  
Vol 26 (24) ◽  
pp. 9960-9976 ◽  
Author(s):  
James P. Kossin ◽  
Timothy L. Olander ◽  
Kenneth R. Knapp
Keyword(s):  
Tropical Cyclone ◽  
Indian Ocean ◽  
Satellite Data ◽  
Formal Analysis ◽  
P Value ◽  
Tropical Cyclone Intensity ◽  
The South ◽  
Cyclone Intensity ◽  
The North ◽  
South Indian

Abstract The historical global “best track” records of tropical cyclones extend back to the mid-nineteenth century in some regions, but formal analysis of these records is encumbered by temporal heterogeneities in the data. This is particularly problematic when attempting to detect trends in tropical cyclone metrics that may be attributable to climate change. Here the authors apply a state-of-the-art automated algorithm to a globally homogenized satellite data record to create a more temporally consistent record of tropical cyclone intensity within the period 1982–2009, and utilize this record to investigate the robustness of trends found in the best-track data. In particular, the lifetime maximum intensity (LMI) achieved by each reported storm is calculated and the frequency distribution of LMI is tested for changes over this period. To address the unique issues in regions around the Indian Ocean, which result from a discontinuity introduced into the satellite data in 1998, a direct homogenization procedure is applied in which post-1998 data are degraded to pre-1998 standards. This additional homogenization step is found to measurably reduce LMI trends, but the global trends in the LMI of the strongest storms remain positive, with amplitudes of around +1 m s−1 decade−1 and p value = 0.1. Regional trends, in m s−1 decade−1, vary from −2 (p = 0.03) in the western North Pacific, +1.7 (p = 0.06) in the south Indian Ocean, +2.5 (p = 0.09) in the South Pacific, to +8 (p < 0.001) in the North Atlantic.

scholarly journals The Impact of Targeted Dropwindsonde Observations on Tropical Cyclone Intensity Forecasts of Four Weak Systems during PREDICT

2014 ◽  
Vol 142 (8) ◽  
pp. 2860-2878 ◽  
Author(s):  
Ryan D. Torn
Keyword(s):  
Tropical Cyclone ◽  
Meridional Wind ◽  
Horizontal Wind ◽  
Small Subset ◽  
Tropical Cyclone Intensity ◽  
Ensemble Forecasts ◽  
Cyclone Intensity ◽  
Intensity Forecast ◽  
Tropical Cyclone Intensity Forecasts ◽  
The Impact

Abstract The value of assimilating targeted dropwindsonde observations meant to improve tropical cyclone intensity forecasts is evaluated using data collected during the Pre-Depression Investigation of Cloud-Systems in the Tropics (PREDICT) field project and a cycling ensemble Kalman filter. For each of the four initialization times studied, four different sets of Weather Research and Forecasting Model (WRF) ensemble forecasts are produced: one without any dropwindsonde data, one with all dropwindsonde data assimilated, one where a small subset of “targeted” dropwindsondes are identified using the ensemble-based sensitivity method, and a set of randomly selected dropwindsondes. For all four cases, the assimilation of dropwindsondes leads to an improved intensity forecast, with the targeted dropwindsonde experiment recovering at least 80% of the difference between the experiment where all dropwindsondes and no dropwindsondes are assimilated. By contrast, assimilating randomly selected dropwindsondes leads to a smaller impact in three of the four cases. In general, zonal and meridional wind observations at or below 700 hPa have the largest impact on the forecast due to the large sensitivity of the intensity forecast to the horizontal wind components at these levels and relatively large ensemble standard deviation relative to the assumed observation errors.

scholarly journals The Role of Inner-Core Moisture in Tropical Cyclone Predictability and Practical Forecast Skill

2017 ◽  
Vol 74 (7) ◽  
pp. 2315-2324 ◽  
Author(s):  
Kerry Emanuel ◽  
Fuqing Zhang
Keyword(s):  
Tropical Cyclone ◽  
Wind Field ◽  
Inner Core ◽  
Forecast Skill ◽  
Initial Condition ◽  
Tropical Cyclone Intensity ◽  
Cyclone Intensity ◽  
Storm Intensity ◽  
Tropical Cyclone Intensity Forecasts

Abstract Errors in tropical cyclone intensity forecasts are dominated by initial-condition errors out to at least a few days. Initialization errors are usually thought of in terms of position and intensity, but here it is shown that growth of intensity error is at least as sensitive to the specification of inner-core moisture as to that of the wind field. Implications of this finding for tropical cyclone observational strategies and for overall predictability of storm intensity are discussed.

scholarly journals Relating the Skill of Tropical Cyclone Intensity Forecasts to the Synoptic Environment

2013 ◽  
Vol 28 (4) ◽  
pp. 961-980 ◽  
Author(s):  
Kieran T. Bhatia ◽  
David S. Nolan
Keyword(s):  
Tropical Cyclone ◽  
Environmental Conditions ◽  
Forecast Error ◽  
Statistical Significance ◽  
Absolute Error ◽  
Tropical Cyclone Intensity ◽  
Hurricane Intensity ◽  
Cyclone Intensity ◽  
Additional Information ◽  
Tropical Cyclone Intensity Forecasts

Abstract Prior knowledge of the performance of a tropical cyclone intensity forecast holds the potential to increase the value of forecasts for end users. The values of certain dynamical parameters, such as storm speed, latitude, current intensity, potential intensity, wind shear magnitude, and direction of the shear vector, are shown to be related to the error of an individual model forecast. The varying success of each model in the different environmental conditions represents a source of additional information on the reliability of an individual forecast beyond average forecast error. Three hurricane intensity models that were operational for the duration of the five hurricane seasons between 2006 and 2010, as well as the National Hurricane Center official forecast (OFCL), are evaluated for 24-, 48-, and 72-h forecasts in the Atlantic Ocean. The performance of each model is assessed by computing the mean absolute error, bias, and percent skill relative to a benchmark model. The synoptic variables are binned into physically meaningful ranges and then tested individually and in combinations to capture the different regimes that are conducive to forecasts with higher or lower error. The results address conventional wisdom about which environmental conditions lead to better forecasts of hurricane intensity and highlight the different strengths of each model. The statistical significance established between the different bins in each model as well as the corresponding bins for other models indicates there is the potential for error predictions to accompany tropical cyclone intensity forecasts.

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