| dc.identifier.uri | http://hdl.handle.net/11401/77774 | |
| dc.description.sponsorship | This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree. | en_US |
| dc.format | Monograph | |
| dc.format.medium | Electronic Resource | en_US |
| dc.language.iso | en_US | |
| dc.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dc.type | Dissertation | |
| dcterms.abstract | Cool-season extratropical cyclones near the U.S. East Coast often have significant impacts on the safety, health, environment and economy of this most densely populated region. For example, the “January 2015 blizzard†caused thousands of flights cancellations, travel bans enacted in five states, and two related deaths. Hence it is of vital importance to forecast these high-impact winter storm events as accurately as possible by numerical weather prediction (NWP), including in the medium-range (3-6 days). Ensemble forecasts are appealing to operational forecasters when forecasting such events because they can provide an envelope of likely solutions to serve user communities. However, it is generally accepted that ensemble outputs are not used efficiently in NWS operations mainly due to the lack of simple and quantitative tools to communicate forecast uncertainties and ensemble verification to assess model errors and biases. Ensemble sensitivity analysis (ESA), which employs a linear correlation and regression between a chosen forecast metric and the forecast state vector, can be used to analyze the forecast uncertainty development for both short- (1─2 days) and medium-range forecasts. The application of ESA to a high-impact winter storm in December 2010 demonstrated that the sensitivity signals based on different forecast metrics (the EOF PCs, the MSLP run cycle differences, and the short-range forecast errors) are robust. In particular, the ESA based on the leading two EOF PCs can separate sensitive regions associated with cyclone amplitude and intensity uncertainties, respectively. The sensitivity signals were verified using the leave-one-out cross validation (LOOCV) method based on a multi-model ensemble from CMC, ECMWF, and NCEP. The climatology of ensemble sensitivities for the leading two EOF PCs based on 3-day and 6-day forecasts of historical cyclone cases was presented. It was found that the EOF1 pattern often represents the intensity variations while the EOF2 pattern represents the track variations along west-southwest and east-northeast direction. For PC1, the upper-level trough associated with the East Coast cyclone and its downstream ridge are important to the forecast uncertainty in cyclone strength. The initial differences in forecasting the ridge along the west coast of North America impact the EOF1 pattern most. For PC2, it was shown that the shift of the tri-polar structureï€the East Coast trough and its adjacent ridgesï€is most significantly related to the cyclone track forecasts. The EOF/fuzzy clustering tool was applied to diagnose the scenarios in operational ensemble forecast of East Coast winter storms. It was shown that the clustering method could efficiently separate the forecast scenarios associated with East Coast storms based on the 90-member multi-model ensemble. A scenario-based ensemble verification method has been proposed and applied it to examine the capability of different EPSs in capturing the analysis scenarios for historical East Coast cyclone cases at lead times of 1ï€9 days. The results suggest that the NCEP model performs better in short-range forecasts in capturing the analysis scenario although it is under-dispersed. The ECMWF ensemble shows the best performance in the medium range. The CMC model is found to show the smallest percentage of members in the analysis group and a relatively high missing rate, suggesting that it is less reliable regarding capturing the analysis scenario when compared with the other two EPSs. A combination of NCEP and CMC models has been found to reduce the missing rate and improve the error-spread skill in medium- to extended-range forecasts. By utilizing the scenario analysis, whether the ensemble mean from the multi-model ensemble or each individual model is really better than other subsets of an ensemble forecast has also been analyzed. It was found that in the majority of cases, the analysis does not lie within Group EM in the multi-mode ensemble. Meanwhile, the quadrant statistics suggest that the ECMWF model misses the analysis direction in a majority of past storms although it shows a slightly higher chance to be in the analysis quadrant in the medium range than the other two EPSs. Based on the orthogonal features of the EOF patterns, the model errors for 1ï€6-day forecasts have been decomposed for the leading two EOF patterns. The results for error decomposition show that the NCEP model tends to better represent both EOF1 and EOF2 patterns by showing less intensity and displacement errors during 1ï€3 days. The ECMWF model is found to have the smallest errors in both EOF1 and EOF2 patterns during 4ï€6 days. The CMC model shows moderate errors for days 1ï€2 and the largest errors for days 3ï€6. We have also found that East Coast cyclones in the ECMWF forecast tend to be towards the southwest of the other two models in representing the EOF2 pattern, which is associated with the southwest-northeast shifting of the cyclone. This result suggests that ECMWF model may have a tendency to show a closer-to-shore solution in forecasting East Coast winter storms The downstream impacts of Rossby wave packets (RWPs) on the predictability of winter storms are investigated to explore the source of ensemble uncertainties. The composited RWPA anomalies show that there are enhanced RWPs propagating across the Pacific in both large-error and large-spread cases over the verification regions. There are also indications that the errors might propagate with a speed comparable with the group velocity of RWPs. Based on the composite results as well as our observations of the operation daily RWPA, a conceptual model of errors/uncertainty development associated with RWPs has been proposed to serve as a practical tool to understand the evolution of forecast errors and uncertainties associated with the coherent RWPs originating from upstream as far as western Pacific. It suggests that the central and the leading regions of the RWP are the preferable regions for large errors/uncertainties to grow and develop. The errors and spread in a case study for a coherent RWP fit the conceptual model well. The ESA is also performed for this case study and the corresponding sensitivities also qualitatively fit the conceptual model. To investigate the mechanism of how RWPs affect the downstream predictability, the forecasts errors associated with the downstream development have been investigated under the framework of eddy kinetic energy (EKE) budget based on the same RWP case. The results show that the errors in the total advection term contribute significantly to the errors in the local EKE tendency especially over the eastern U.S. and western Atlantic region. The errors in the ageostrophic flux term play a significant role in the initial development of the EKE errors. A comparison between two ensemble members shows that the ensemble member that has less errors better resolved the initial EKE center with less AGFD errors, leading to a much better forecast of the EKE tendency over the downstream areas (eastern U.S. and western Atlantic). This work shed lights on improving the understanding of winter storm predictability and NWP model bias and provides new perspectives to communicate forecast uncertainty in predicting cool-season HIW events. | |
| dcterms.available | 2017-09-20T16:53:33Z | |
| dcterms.contributor | Hameed, Sultan | en_US |
| dcterms.contributor | Chang, Edmund K.M. | en_US |
| dcterms.contributor | Colle, Brian A. | en_US |
| dcterms.contributor | Zhang, Minghua | en_US |
| dcterms.contributor | Majumdar, Sharanya J. | en_US |
| dcterms.creator | Zheng, Minghua | |
| dcterms.dateAccepted | 2017-09-20T16:53:33Z | |
| dcterms.dateSubmitted | 2017-09-20T16:53:33Z | |
| dcterms.description | Department of Marine and Atmospheric Science | en_US |
| dcterms.extent | 302 pg. | en_US |
| dcterms.format | Monograph | |
| dcterms.format | Application/PDF | en_US |
| dcterms.identifier | http://hdl.handle.net/11401/77774 | |
| dcterms.issued | 2016-12-01 | |
| dcterms.language | en_US | |
| dcterms.provenance | Made available in DSpace on 2017-09-20T16:53:33Z (GMT). No. of bitstreams: 1
Zheng_grad.sunysb_0771E_13116.pdf: 27194738 bytes, checksum: 411f6ffab6a671e5a7cc1f104cb4c51e (MD5)
Previous issue date: 1 | en |
| dcterms.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dcterms.subject | cyclone, ensemble verification, predictability, Rossby wave packets | |
| dcterms.subject | Atmospheric sciences -- Meteorology | |
| dcterms.title | Growth of Errors and Uncertainties in Medium Range Ensemble Forecasts of U.S. East Coast Cool Season Extratropical Cyclones | |
| dcterms.type | Dissertation | |
