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Hong-Bo Liu
,
Jing Yang
,
Da-Lin Zhang
, and
Bin Wang

Abstract

During the mei-yu season of the summer of 2003, the Yangtze and Huai River basin (YHRB) encountered anomalously heavy rainfall, and the northern YHRB (nYHRB) suffered a severe flood because of five continuous extreme rainfall events. A spectral analysis of daily rainfall data over YHRB reveals two dominant frequency modes: one peak on day 14 and the other on day 4 (i.e., the quasi-biweekly and synoptic-scale mode, respectively). Results indicate that the two scales of disturbances contributed southwesterly and northeasterly anomalies, respectively, to the mei-yu frontal convergence over the southern YHRB (sYHRB) at the peak wet phase. An analysis of bandpass-filtered circulations shows that the lower and upper regions of the troposphere were fully coupled at the quasi-biweekly scale, and a lower-level cyclonic anomaly over sYHRB was phase locked with an anticyclonic anomaly over the Philippines. At the synoptic scale, the strong northeasterly components of an anticyclonic anomaly with a deep cold and dry layer helped generate the heavy rainfall over sYHRB. Results also indicate the passages of five synoptic-scale disturbances during the nYHRB rainfall. Like the sYHRB rainfall, these disturbances originated from the periodical generations of cyclonic and anticyclonic anomalies at the downstream of the Tibetan Plateau. The nYHRB rainfalls were generated as these disturbances moved northeastward under the influence of monsoonal flows and higher-latitude eastward-propagating Rossby wave trains. It is concluded that the sYHRB heavy rainfall resulted from the superposition of quasi-biweekly and synoptic-scale disturbances, whereas the intermittent passages of five synoptic-scale disturbances led to the flooding rainfall over nYHRB.

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Mei Hong
,
Ren Zhang
,
Dong Wang
,
Min Wang
,
Kefeng Liu
, and
Vijay P. Singh

Abstract

A new dynamical–statistical forecasting model of the western Pacific subtropical high (WPSH) area index (AI) was developed, based on dynamical model reconstruction and improved self-memorization, in order to address the inaccuracy of long-term WPSH forecasts. To overcome the problem of single initial prediction values, the self-memorization function was introduced to improve the traditional reconstruction model, thereby making it more effective for describing chaotic systems, such as WPSH. Processing actual data, the reconstruction equation was used as a dynamical core to overcome the problem of employing a simple core. The resulting dynamical–statistical forecasting model for AI was used to predict the strength of long-term WPSH forecasting. Based on 17 experiments with the WPSH during normal and abnormal years, forecast results for a period of 25 days were found to be good, with a correlation coefficient of ~0.80 and a mean absolute percentage error of <8%, showing that the improved model produced satisfactory long-term forecasting results. Additional experiments for predicting the ridgeline index (RI) and the west ridge-point index (WI) were also performed to demonstrate that the developed model was effective for the complete prediction of the WPSH. Compared with the authors’ previous models and other established models of reasonable complexity, the current model shows better long-term WPSH forecasting ability than do other models, meaning that the aberrations of the subtropical high could be defined and forecast by the model.

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Chidong Zhang
,
Aaron F. Levine
,
Muyin Wang
,
Chelle Gentemann
,
Calvin W. Mordy
,
Edward D. Cokelet
,
Philip A. Browne
,
Qiong Yang
,
Noah Lawrence-Slavas
,
Christian Meinig
,
Gregory Smith
,
Andy Chiodi
,
Dongxiao Zhang
,
Phyllis Stabeno
,
Wanqiu Wang
,
Hong-Li Ren
,
K. Andrew Peterson
,
Silvio N. Figueroa
,
Michael Steele
,
Neil P. Barton
,
Andrew Huang
, and
Hyun-Cheol Shin

Abstract

Observations from uncrewed surface vehicles (saildrones) in the Bering, Chukchi, and Beaufort Seas during June–September 2019 were used to evaluate initial conditions and forecasts with lead times up to 10 days produced by eight operational numerical weather prediction centers. Prediction error behaviors in pressure and wind are found to be different from those in temperature and humidity. For example, errors in surface pressure were small in short-range (<6 days) forecasts, but they grew rapidly with increasing lead time beyond 6 days. Non-weighted multimodel means outperformed all individual models approaching a 10-day forecast lead time. In contrast, errors in surface air temperature and relative humidity could be large in initial conditions and remained large through 10-day forecasts without much growth, and non-weighted multimodel means did not outperform all individual models. These results following the tracks of the mobile platforms are consistent with those at a fixed location. Large errors in initial condition of sea surface temperature (SST) resulted in part from the unusual Arctic surface warming in 2019 not captured by data assimilation systems used for model initialization. These errors in SST led to large initial and prediction errors in surface air temperature. Our results suggest that improving predictions of surface conditions over the Arctic Ocean requires enhanced in situ observations and better data assimilation capability for more accurate initial conditions as well as better model physics. Numerical predictions of Arctic atmospheric conditions may continue to suffer from large errors if they do not fully capture the large SST anomalies related to Arctic warming.

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