African american speed dating near Panzhihua China

Wawu Mountain, the key scenic spot of the forest park, is a famous Taoist shrine. Together with Mt. Emei it is reputed to be one of the two wonders of Sichuan dating back to the Tang and Song dynasties. Meishan is 63 kilometers 39 miles from Chengdu and about 80 kilometers 50 miles from Chengdu Shuangliu International Airport.

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The city has four distinct seasons and is characterized by moderate weather that reflects a subtropical, humid climate. There is an abundance of rainfall during the monsoon season accompanied by less sunlight. Due to the general terrain of the city, which is higher in the east and lower in the west, the annual temperature varies with the elevation changes. July and August are the hottest periods, and massive bursts of precipitation become more common from May to September. History: An administrative organization was first established in Meishan in the year during the Northern and Southern Dynasties.

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It was known then as Meishou, but today it is well known by all Chinese as the hometown of Su Dongpo Su Shi and his two sons, all of whom are great poets of the Northern Song Dynasty Physical Features: The Minjiang and Qingyi rivers flow through Meishan, creating broad alluvial plains along them. Most of its western and eastern regions geographically consist of low mountainous areas. Answers App. Meishan Facts. Like price, times, etc.

Meishan Sichuan: Home to Famous Chinese Litterateur Su Shi

Thank you. Hi, there is no direct bus between the two places, you need to take a bus from Meishan Bus Station to Hongya Bus Station first. The bus departs from to at an interval about 1 hour and it costs about CNY17 per person. After that, take a bus from Hongya Bus Station to Wawushan but there is no specific times of the bus. It costs about CNY20 per person.

Which Dashiqiao Bus Stop do you mean? Since our only constraint is a temperature trend, we start with a bootstrap ensemble of daily temperature observations resampled from the training period. A set of heuristic rules for the resampling ensures that the ensemble members reproduce the prescribed temperature trend and realistic annual cycles see Fig.

Inspired by the moving block bootstrap Efron and Tibshirani ; Lahiri , we bootstrap blocks of temperature observations of day length rather than single-day observations e. Experiments with different block lengths suggest that day blocks yield bootstrap series with realistic persistence behavior physically associated with large-scale circulation patterns. Bootstrapping day blocks of a training period temperature series black for future temperature series gray , constrained by a prescribed future temperature trend black line w.

Temperatures of a suitable block i from the training period and the associated calendar dates taking all weather observations of block i with them are assigned to a block in the future period, conditioned on the temperatures of block i. In this illustrative example, the 12 future days starting at 29 Apr are mapped onto the 12 days starting at 26 Apr Since the bootstrapped temperature observations belong to dates from the training period, the temperature series ensemble can be extended to a bootstrap ensemble of calendar dates assume a single-station setting for now, for regional projections see below.

Each ensemble member therefore consists of a date-to-date-mapping,. A future temperature series generated by applying f reproduces the prescribed temperature trend, which means that. Note that trends other than linear ones are feasible, although this may lead to convergence problems. For details about how f is constructed, refer to Orlowsky et al. By applying f , future series for any kind of observations available at the dates of the training period can be obtained, for example, station observations, monsoon indices, and river runoff and typhoon occurrences.

Thus conserving past weather information in the future bootstrap series is equivalent to the analog approaches. For regional multistation projections, a preparatory step identifies climatological subregions by a hierarchical cluster analysis based on temperature and precipitation. Individual temperature trends are prescribed to one representative station of each subregion. This reduces the complexity and allows for the representation of spatial patterns of future climate variables in the constraints. Here, five subregions and representative stations are identified see Fig.

Note that, besides determining the representative stations and their trends, no adaptation or calibration specific to the region of interest is necessary. Owing to its design, our approach produces conservative projections in the sense that, if the prescribed future trend continues the training period trend, any systematic change in the set of observables linked to the temperature trend of the training period will continue in the future period. For example, if during the training period a warming is observed and for the future a further warming is prescribed, then any trends linked to the observed warming will be likely to continue in the future projections.

This agrees with the intuition that, on the near-future time scale which we are dealing with here , changes of the physical and statistical relationships within the climate system are small. Bootstrapping schemes in general tend to reduce variability and persistence. Although sometimes detectable, these effects are weak in STAR projections [see the case study in Orlowsky et al.

STAR is implicitly based on the important assumption that joint statistical properties of the different meteorological observables are the same in the training and the future period. This is almost certainly not the case for a changing climate and, in particular, not for the end of the future period with the strongest warming.

However, cross-validations as in Orlowsky et al. To generate climates with warmer average temperatures than in the training period, STAR has to preferably select warm blocks like the intentionally biased bootstrap would do , in particular for the end of the future period with the most elevated temperatures , which can reduce the size of the sample of blocks considerably.

This effect is shown for one representative station Fig. It is obvious that the use of high summer temperatures becomes more and more frequent and that low temperatures are used less by the end of the future period.

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The bandwidth and the size of the sample from which blocks can be chosen at the end of the future period is thus narrowed. This leads to a decrease of the amplitude of the annual cycle and to a reduced variability at the end of the future period. In this case the imposed trend does not lead to a statistically visible reduction of variability. Experiments with different prescribed trends suggest that according to this criterion the warming in the training period can continue to the future period with the same strength i. However, from our experience, even larger prescribed future trends can yield satisfactory results, particularly compared to the performance of dynamical models Orlowsky et al.

STAR resamples observational station data to generate ensembles of future climate projections, which are constrained by a linear temperature trend. Both observational and GCM data are described section 3a. A cross-validation experiment that studies the applicability of STAR for the Yangtze River basin is presented in section 3b. Daily time series of the following variables are analyzed for the present day training and subsequent future climate projection: temperature, precipitation, Yangtze runoff, a monsoon index, and typhoons in the East China Sea.

The variables are daily minimum, mean and maximum temperature, and precipitation. We use stations along the Yangtze Fig. Since the training dataset ends in , the years from to are chosen as the future period. Station density varies across China, with fewer stations per area in the mountainous regions compared to the eastern plains see Fig. Sielmann , personal communication. To study the typhoons with landfall, we further analyze the tropical cyclone tracks that reach over land in the rectangular area.

Both runs come on a Gaussian grid with a resolution of approximately 1. The twentieth-century run is forced by observed greenhouse gas concentrations; the last year serves to initialize the subsequent A1B run for the twenty-first-century scenario. They range from 0. These increases are much stronger than the ones observed in the training period ranging between 0. Further, note that in GCM simulations, temperature averages often deviate from observations, while temperature changes are more realistic.

The GCM-simulated increases of temperature from to are therefore assumed to start from the temperature levels observed at the representative stations, instead of the respective GCM-simulated temperature levels, of the year Underlying this procedure is the assumption that the temperature bias of the GCM is time independent, which could be problematic since GCMs are known to have drifts.

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However, these drifts are of a smaller order of magnitude over our yr period than the bias itself and should, therefore, not compromise our results. The performance of STAR in the Yangtze River catchment is evaluated in a cross-validation experiment that supplements a model cross-validation for the North Atlantic European sector Orlowsky and Fraedrich It is set up to generate the climate of a validation period from to from the independent preceding time span to The same five representative stations are used as for the future projections Fig.

The prescribed linear temperature trend for the validation period is determined by a regression analysis of the annual mean temperature series at the five representative stations from to ; ensemble members are created.

Generally, these projections do not fulfill the a posteriori variability conservation criterion from section 2b , which means that the climate of the validation period lies outside of the training period variability. A successful cross-validation in spite of this demanding setting gives strong evidence of the robustness of the projections, at least for the long-term statistics considered here. This hints at the robustness of the results, despite the short length of 20 yr of the projected time series.

Note that in a similar cross-validation for the Elbe River catchment Orlowsky et al. For both upper and lower reaches see Fig. Both ensemble average and ensemble spread in parentheses are given. These results suggest that STAR is a suitable tool for future climate projections of the Yangtze River catchment, despite the variability conservation criterion section 2b not being fully satisfied. It transects different climate types—such as plateau, temperate inland, and monsoonal climates—which are controlled by i topography, ii latitude characterizing radiative forcing and seasonality, and iii monsoonal systems induced by the land—sea contrast [e.

This climatological complexity is challenging for statistical climate projections. For the analysis of the observations and bootstrap projections, the course of the Yangtze is approximated by two straight lines see Fig. The two lines cross approximately at the main bend of the Yangtze River near Panzhihua. Upper lower reaches are represented by 8 19 stripes perpendicular to the two lines; station data within each stripe is averaged, leading to a single time series of daily meteorological observations per stripe; the double width of the first stripe is due to station scarcity. Climate statistics of these averaged time series are presented as profiles see Fig.

The future bootstrap ensemble contains projections. None of these comply with the a posteriori variability conservation criterion from section 2b. Strictly speaking, the climatological variability of the training period is, therefore, overstretched by the ECHAM5-derived temperature trends.

However, since our analysis is restricted to long-term statistics and because of the encouraging cross-validation experiment which also does not satisfy the a posteriori variability conservation criterion from section 2b , we assume that the results presented now are not critically affected by this drawback. Before the future projections along the Yangtze are discussed in detail, the averages of the upper and lower reaches are summarized Table 2.