جستجوی مقالات مرتبط با کلیدواژه « heavy precipitation » در نشریات گروه « فیزیک »

Heavy precipitation events often cause irreparable damage to human and economy. The ability to accurate prediction of the occurrence of such phenomena is very important especially for early warning systems run by operational centers. Identifying the main factors involved in heavy precipitation can result in more accurate predictions. The “factor separation method” (FSM) is used for this purpose. Based on the previous studies on factors affecting heavy precipitation especially in the Alpine region of Europe, and with regard to the geographical location of the Tehran Province, four factors are included in the application of the FSM. These factors are (1) the potential vorticity (PV) anomalies in the upper troposphere manifested in the form of PV streamers, (2) the PV anomalies in the middle troposphere, (3) the Alborz mountain range, and (4) the Zagros mountain range. The contributions of these factors were assessed in the heavy snowfall event of the 6th of January 2008 in Tehran by applying the FSM using the ERA-Interim data and the WRF (Weather Research and Forecasting) model. To apply the FSM, 16 simulations, which cover all possible combinations of the four factors, were performed by the WRF model using 3 nested domains with horizontal resolutions of 45, 15, and 5 kilometers, respectively, and 60 vertical levels. These 16 simulations are: the control run (1), the removal of PV anomalies of the upper (2) and the middle (3) levels of the troposphere, the reduction of the heights of the Alborz (4) and Zagros (5) mountain ranges, the removal of two-factor combinations (6 to 12), the removal of three-factor combinations (13 to15), and the removal of the four factors (16). To carry out the experiments involving the removal of PV anomalies, a PV inversion procedure was used to construct the initial states.
On the 6th of January 2008, a case of heavy snowfall occurred in a significant part of the country from Tehran to the west in the south of the Alborz mountains. The case involved the intrusion of cold and dry air with large positive PV anomalies from the stratosphere to the upper troposphere in the form of a PV streamer. The PV streamer acts to substantially decrease the static stability in the lower to the middle troposphere. The resulting PV anomalies are associated with low-level winds, which can provide the low-level moisture supply for the precipitation event. The results show that the PV streamer is the factor with the highest impact. The independent contribution of the PV streamer is about 76.5% as obtained by the detailed analysis of the FSM using the outputs of 16 simulations. Another factor to consider is orography which can block air flows or lead to substantial ascent or descent. The magnitude of the impact due to orography depends on the characteristics of the flows over and around the mountains. The determining properties of the flows are the angle of hitting the mountains and the intensity of the flow. In those simulations that the altitude of the Alborz mountain range was reduced, the dominant air flow was from the windward to the leeward side of the mountain. As a result of the increase in the penetration of the cold air from the windward to the leeward of the Alborz mountain range, the pressure on the windward (leeward) of the mountains reduces (increases). In other words, the pressure gradient is reduced in the Tehran area, when the height of the mountains is reduced. So the warm front in the southern slopes of Alborz mountain ranges is weakened. The results of the corresponding simulations and the application of the FSM show that the contribution of the Alborz mountain range in this case is next to the upper-level PV anomalies. Quantitatively, the absolute contribution of the Alborz-mountain factor is about 48% in the case of the heavy snowfall that occurred in Tehran. Finally, it is worth mentioning that the Zagros mountain range and the PV anomalies of the middle troposphere play negligible role in this case.

Daily precipitation records of Mehrabad synoptic station based in Tehran, for the period 1951–2013 was used to identify moderate to heavy cold weather precipitation events in the mainly rainy season of Iran which starts in October and ends in May. Mehrabad is one of the oldest stations in the country that holds the longest and most complete precipitation records available in the country with very few missing values; thus being suitable for identifying the types of precipitation events for the region and the associated atmospheric circulations. Following the Iranian Meteorological Organization definition, we identified moderate and heavy precipitation events for Tehran Province as the events for which total daily precipitation ranges from 5 to 20 mm and from 20 to 50 mm, respectively; but being characterized with anomalous cold weather conditions. This screening approach has resulted in a set of 133 days of moderate to heavy precipitation events featured with cold weather conditions, which is adequate for implementing a multivariate analysis. The 500 hPa geopotential height and relative vorticity, sea level pressure (SLP), 850 hPa wind field and advection of specific humidity at 00 UTC over the time period considered (October–May), covering a large geographical domain centred on Iran (20°E–70°E, 20°N–55°N) with a 2.5° latitude × 2.5° longitude spatial resolution were retrieved from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis archive (Kalnay et al., 1996; Kistler et al., 2001).
In the present study the S- and T-mode Principal Component Analysis (PCA) were used for classifying the 500 hPa atmospheric circulations associated with the 133 precipitation events. The S-mode PCA with correlation as a similarity measure was used as a data reduction tool and pre-processor of K-means clustering method, while a T-mode PCA with correlation as a similarity measure was employed to classify 500 hPa atmospheric circulations independently. Based on the scree plot (Cattel, 1966) and the sampling errors of the eigenvalues (North et al., 1982) five and six PCs were retained for, respectively, for the S- and T-mode PCA applications. The retained PCs were orthogonally and obliquely rotated using varimax and promax criteria, respectively. For an S-mode PCA, we used varimax rotated PC scores as input for K-means clustering, resulting in 5 circulation types (CTs). But applying a T-mode PCA coupled with varimax (promax) rotation classified all the considered days into six CTs. The skills of K-means clustering and un-rotated, varimax and promax rotated T-mode PCA in classifying atmospheric circulations were examined using some indicators measuring the separability and equability of the identified groups of each classification method. The results suggest that the obliquely rotated T-mode PCA outperforms both K-means clustering and orthogonally rotated T-mode PCA in classifying atmospheric circulations.
Each of the six CTs identified are capable of producing significant precipitation in Tehran, but all cases of heavy daily precipitation above 40 mm belong to the CT1 and CT2. Although various forms of tilt in mid-tropospheric geopotential trough are observed among the CTs, but the dominant tilting is that of the northeast–southwest direction, indicating the anti-cyclonic wave breaking. Except CT5, the CTs are associated with a dipolar structure in surface temperature anomaly consisting of a pair of negative and positive anomalies to the west and east of the country, respectively.