July 8, 2022-15.
Because the temperature in each region is different, the entry and exit time of plum blossoms in 2022 is also different. However, plum blossoms generally enter plum in mid-June, and bloom in the first half of July, lasting about 20 days. However, there are also cases where plum blossoms recruit late and leave late. For example, in the Meiyu area in 2020, plum blossoms enter the plum blossom early and leave the plum blossom late, lasting for a long time. At that time, Zhejiang officially entered the rainy season at the end of May, ten days earlier than before.
Generally speaking, the rainy season in 2022 will start in early June and end in early July, lasting about 20 days. It is expected that around June 10, plum blossoms will officially enter all places, and the plum blossom time will be in mid-July. When the rainy season comes, we must pay attention to things at home, check more and don't get moldy.
Taizhou, Jiangsu _ Meiyu 2022
1.2022 When will Jiangsu enter Mei?
In 2022, the rainy season in Jiangsu officially entered May on June 23rd. According to the latest press conference held by Jiangsu Meteorological Observatory and Nanjing Meteorological Observatory, it was announced that Nanjing officially entered the rainy season on June 23rd. In addition, the area south of Huaihe River in Jiangsu Province is also expected to enter Mei on June 23, so the rainy season in Jiangsu this year is Thursday, June 23.
1. Is Jiangsu a late bloomer this year?
It belongs to late plum blossom. Because the average plum blossom day is June 19, this year's plum blossom day is June 23, which is a bit late. Because the Meiyu belt swings from north to south, there are many strong convective weather, with obvious intermittent precipitation and staged high temperature. At the same time, Huaibei will also enter a rainy period from June 23.
2. What's the weather like in May rainy season in Jiangsu this year?
According to the latest introduction of the chief forecaster of Jiangsu Meteorological Observatory, the high temperature weather in our province will continue in the early rainy season this year, and strong convective weather will become more frequent. After June 24, it is expected that there will be short-term heavy precipitation, thunderstorms and even hail weather in the central and northern parts of Jiangsu Province, and more precautions should be taken. It is estimated that there will be two obvious rainfall processes in Jiangsu in the coming week, from the night of 22nd to 24th and from 27th to 28th respectively. From 23 to 26, there were strong convective weather such as short-term heavy precipitation, thunderstorms and small hail. On the 22nd, there was high temperature above 35℃ in central and northern China, along the Yangtze River, in southern Jiangsu, Huaihe River and Huaibei.
3. What is the amount of plum rain in Jiangsu this year?
The average amount of plum rain is 200-260mm. In the meantime, the average rainfall in Huaibei area is 170-230mm, which is more than normal.
2. When will Jiangsu plum blossom bloom in 2022?
According to the latest introduction by the chief forecaster of Jiangsu Meteorological Observatory, it is expected to bloom in mid-July and May, 2022. The duration of plum rains in Jiangsu in recent years is as follows:
1.202 1 Jiangsu Meteorological Observatory issued a forecast of plum rain, and some areas south of Huaihe River officially entered the plum rain on June 13.
2. In 2020, the rainy season in Jiangsu began on June 9th and ended on July 2nd1day, lasting for 43 days.
3.20 19 Jiangsu entered the rainy season from June 18 to July 2 1 day. The total length of the Meiyu period is 33 days, which is longer than the normal Meiyu period of 23 to 24 days.
4.20 16 Jiangsu rainy season lasts for 32 days.
Generally speaking, the rainy season in Jiangsu Province officially entered Mei on June 23, 2022, and generally came out in July. According to the latest weather forecast in Jiangsu Province, plum blossoms will appear in early July this year.
Has Wuxi Huangmei Day passed? 2022
The rainy season in 2022 occurs from late May to late June. Because the rainy season occurs in the two solar terms of mango and xiaoxia every year, this year's mango is June 6 and xiaoxia is July 7.
Therefore, it is expected that the rainy season in the middle and lower reaches of the Yangtze River in China will begin in early June, but according to the time of entering Mei in previous years, it is not uniform and will be separated by a few days. Like Shanghai 202 1 Entering May10; Suzhou, Jiangsu entered Mei in June 10, and the area south of Huaihe River entered Mei in June 13.
Attention.
Criteria for entering the plum blossom in 2022: the average temperature exceeds 22℃ for five consecutive days, and four rainy days are regarded as entering the plum blossom. According to the latest weather forecast in Shanghai, it has not officially entered Mei, and the lowest temperature is still between 16- 18 degrees.
Trends in 2020-2022
Big data epidemic observation: has the national epidemic peak passed?
Research on Teng Jing's Macro-financial Trend
2022- 12-23 17: 23 from Beijing.
Teng Jing hong yun
65438+February 23, 2022
Big data epidemic observation: has the national epidemic peak passed?
-High-frequency simulation and prediction based on Tengjing AI
Tengjing High Frequency and Macro Research Team
Highlights of this issue:
In view of whether the forecast is accurate and whether the national epidemic situation has peaked, we have increased the daily data of subway passenger flow in 28 cities for auxiliary judgment. Lack of non-netizen samples may lead to biased prediction results.
Big data is still not perfect, and the application of big data to macroeconomic forecasting is still not perfect. We analyzed the reasons why Google Flu Trend failed. The reasons may include: extensive media coverage of Google flu trends has led to changes in people's search behavior, which in turn will affect the forecast results of GFT.
At present, the national epidemic may not have reached its peak, but the peak process may be advanced. With the help of subway passenger flow data, it can be judged that Beijing, Shijiazhuang, Wuhan, Chongqing and other cities have passed the peak of the epidemic, while Chengdu, Tianjin, Changsha, Nanjing, Xi 'an and other cities have not yet reached the peak.
First, is the forecast accurate? Mutual Verification of Expectation and Reality
In the last report of "Big Data Epidemic Observation: Central Cities Take the Lead to Peak", we analyzed and predicted that the epidemic situation in some cities in Beijing and Hebei has reached an "inflection point", and cities such as Chengdu and Kunming will gradually peak. The data of Baidu search index shows that Beijing Baidu's "fever" search index continues to decline, and the "cough" search index reaches its peak after "fever", which basically confirms the prediction of our model. However, we also noticed that the nationwide "fever" index reached its peak in June 5438+February 65438+July 2022. Does this mean that the national epidemic has peaked? If so, this data is different from the judgment of some epidemic prevention experts before and after the Spring Festival. Some experts also believe that the national epidemic may not have reached its peak, but the process has been shortened.
However, according to ByteDance's "massive calculation", the peak of Tik Tok's "fever" search index is 65438+February 65438+July, but the headline "fever" search index is still fluctuating upward. According to Zhihu's "Data Emperor" forecast, most provinces and cities will reach the peak of infection around February 20, 2022. Therefore, many researchers want to confirm whether there is a single-day new infection peak nationwide on February 23, 2022. Some people think that the prediction is very accurate, which is more in line with their knowledge of the epidemic on the Internet these days. Some people think it's inaccurate. They think that all their relatives and friends are Yang, and the progress bar is less than half. There is a big difference between personal feelings and predicted results.
At the same time, we noticed that around 65438+February 65438+June in 2022, the "fever" search index of almost all cities and provinces in China ushered in a pulse-like growth of "first rising and then suppressing", and the subsequent daily data was no longer higher than the value of 65438+June. Does this mean that the most difficult stage of the epidemic has passed? Through the data mining and modeling analysis of Baidu and headline epidemic search engine data, it can provide important reference for the future trend judgment of epidemic situation. However, we understand that more data need to be introduced in order to quantitatively evaluate the progress of the epidemic.
Because there is no authoritative data as a reference, the predictions of various epidemic situations are only based on intuitive reasoning or deductive models with parameters, and the predictions are inaccurate, and there is no objective authority to compare the results. Therefore, it is difficult to objectively measure the accuracy of the forecast. All the audience and readers who participated in this prediction can only verify the prediction results through microscopic data and the spread degree of the surrounding epidemic. The infection sequence of different people in a city and the rhythm of infection peak in different cities will have different understandings of the accuracy of prediction.
The model has limitations, applicability of logical assumptions and lack of authoritative data for verification. Is there no need to predict? Thomas kuhn and karl popper launched the most influential debate on the concept of "philosophy of science" in the 20th century. They all questioned the basic premise of science from a philosophical point of view in their own way. Kuhn's "The Structure of Scientific Revolution" points out that even if the results predicted by the existing paradigm have counterexamples in reality, existing scientists will not think that there is something wrong with its paradigm; Only when a new scientific paradigm that can replace the existing paradigm appears and reaches a certain number of counterexamples can the existing scientific paradigm be falsified and a scientific revolution occur. From a critical point of view, the process of negative prediction is also the process of discovering new prediction methods.
Karl Karl popper, a philosopher admired by george soros of Quantum Fund, holds the most famous view that science is conducted through "falsifiability"-people can't prove that the hypothesis is correct, or even get the evidence of truth through induction, but if the hypothesis is wrong, they can refute it. Popper believes that only theoretical systems that can be falsified by experience should be given real scientific status. Therefore, Popper advocates bold assumptions and constantly tries to make mistakes and corrections through falsification, instead of putting forward assumptions and then looking for evidence to support his theory. Falsification is also a way of thinking that Soros has always advocated and practiced.
Two, the subway passenger flow as an important auxiliary observation index of the epidemic peak.
So we start with the epidemic, return to the economy, and verify the peak of the epidemic from multiple dimensions. The passenger flow of the subway is undoubtedly a good observation index. The passenger flow of a city with a subway is affected by several factors: 1, travel control, 2, willingness to travel, and 3, convenience of the subway.
From the data point of view, Beijing and Shanghai, as the two cities with the largest number of subways in China, are also the two cities with the highest average daily passenger flow. The subway data reflects the epidemic level, and the daily data of subway passenger flow lags behind 1-3 days, which is relatively timely. From the point of view of data collection, the subway data comes from the automatic collection of IOT devices, and the influence of manual intervention is small. The data is completely objective and can be regarded as a secondary epidemic.
Photo: Shanghai subway passenger flow
▲ Data source: Wind, Tengjing AI economic forecast.
The above picture shows the passenger traffic data of Shanghai Metro from June 20 19 to the present. More obvious are the Wuhan epidemic in early 2020, the Shanghai epidemic in April 2022 and the national epidemic in June 2022. Because the subway passenger flow follows the principle of high from Monday to Friday and low on Saturday and Sunday, the daily data information is somewhat redundant. Later, short-term intra-day data fluctuations can be filtered by comparing the average data of Zhou Du.
Photo: Shanghai subway passenger flow
▲ Data source: Wind, Tengjing AI economic forecast.
Comparing the passenger flow of Beijing subway, we can also see that in April 2022, the Shanghai subway was shut down for about 7 weeks. Although Beijing has not stopped running, the average passenger volume of Zhoudu subway has dropped from 8 million in the past three years to less than 6,543,800+. It is worth noting that the passenger flow of Beijing subway after September 2022 is significantly lower than that of Shanghai. On the one hand, the epidemic situation, on the other hand, the Beijing subway needs 72 hours of nucleic acid inspection, and1October 24 165438 was further shortened to 48 hours, and this policy was lifted on February 5, 65438.
Figure: Beijing subway passenger flow
▲ Data source: Wind, Tengjing AI economic forecast.
Figure: The 7-day moving average of subway passenger flow in ten major cities is highly consistent in coordination.
▲ Data source: Wind, Tengjing AI economic forecast.
Based on this data, we think that the epidemic peak in Beijing has passed, but the overall epidemic peak in China has not reached the peak as indicated by Baidu search index and headline index, but is in a period of rapid development. We set up a four-stage data model to help verify whether the city has reached its peak. As shown below, the subway passenger flow in Beijing, Wuhan, Chongqing, Shenyang, Shijiazhuang, Lanzhou and Kunming has stabilized and rebounded, and is currently in the fourth stage; Chengdu, Tianjin, Changchun, Zhengzhou, Guangzhou, Xiamen, Shenzhen, Xi 'an, Shanghai, Nanjing and other cities are still in the third peak stage. Because the moving average may bring data lag, we did a test with real data later.
Figure: The process of epidemic spread
▲ Data source: Tengjing AI economic forecast
Figure: Subway passenger flow in some cities in China
Note: The top ten cities refer to Beijing, Shanghai, Guangzhou, Chengdu, Nanjing, Wuhan, Xi, Suzhou, Zhengzhou and Chongqing, the same below.
▲ Data source: Wind, Tengjing AI economic forecast.
The progress of the epidemic in days, if the subway travel data picks up that day, mainly depends on two data, the first is the year-on-year and the second is the chain.
Judging from the daily data, Beijing subway travel is on the rise both month-on-month and year-on-year, which is consistent with the judgment of peaking. Other cities that may reach the peak are Wuhan, Chongqing and Chengdu. Passenger traffic in Shanghai, Guangzhou, Nanjing, Suzhou and Xi 'an continued to decline, indicating that the epidemic is still in the process of reaching its peak.
Figure: Subway passenger flow in some cities in China
▲ Data source: Wind, Tengjing AI economic forecast.
Due to the serious year-on-year decline in subway passenger traffic data, we judge that the epidemic situation in Shanghai, Guangzhou, Nanjing, Xi 'an, Suzhou, Zhengzhou and other cities is still in the process of reaching its peak, while Beijing, Wuhan and Chongqing have turned positive year-on-year, and the peak of the epidemic situation is expected to have passed.
Figure: Comparison of subway passenger flow in 28 cities with that in Zhou Du.
▲ Data source: Wind, Tengjing AI economic forecast.
Third, how do expectations interact with reality?
There is a lot of experience after the liberalization of epidemic control. No matter the rhythm of epidemic peak and its influence on consumption and labor participation rate, many countries can refer to it. This undoubtedly gives us some expectations. The liberalization of the population of 654.38+0.4 billion is different from that of countries with middle population. Domestic infectious disease experts also said in various media that the epidemic will peak around the Spring Festival in the first quarter of next year, releasing such a signal that it will peak in the future. But from the perception of Beijing and most cities, it seems that the epidemic peaked earlier than our cognition, so what will happen?
Failure of Policy Indicators: Goodhart's Law
When most Internet participants know that Baidu search index can indirectly represent the epidemic situation, it may not be accurate. To some extent, it is the embodiment of Goodhart's law in the epidemic. Goodhart's law comes from the statement of Charles goodhart, a British economist, which means that when a policy becomes a goal, it is no longer a good policy. One of the explanations is that once a social indicator or economic indicator becomes an established goal to guide macro-policy formulation, it loses its original information value.
Undoubtedly, when most people don't know the importance of "Baidu popularity index", it is still effective with a high probability. The connotation logic is that the search volume of big data indirectly reflects the spontaneous online search behavior of most residents, and "fever" search is the same as positive and symptomatic to some extent. However, when the official media and self-media are reporting, this indicator will lead to more searches, which have nothing to do with the epidemic itself, but the effect brought by Internet traffic.
The deviation of netizens' search behavior may cause data pollution.
We compared the subway passenger flow of Shijiazhuang, Lanzhou, Beijing, Wuhan, Chongqing, Shenyang, Kunming, Chengdu, Tianjin and other cities, and found that they all experienced the data change pattern of policy relaxation, epidemic situation climbing down, epidemic situation peaking and then rising. At present, most cities are still in the stage of rising epidemic and declining passenger flow. The peak of the national epidemic has not yet arrived, but the "fever" search index given by Baidu Index has reached its peak. We judge that Baidu's "fever" search index on or after 12 and 16 may be abnormal. The core logic is 12, 16, and all cities in China have appeared.
Sample missing: elderly people aged 60 and above are not netizens.
We know that Baidu index, headline index and micro-index are all data products based on massive netizen behavior data, so the behavior data of non-netizens are naturally excluded from the research sample.
According to the 50th Statistical Report on the Development of Internet in China released by China Internet Information Center on August 3rd, 2022, by June, 2022, the number of non-netizens in China was 362 million, which is not a small base. From a regional perspective, non-netizens in China are still mainly in rural areas, and the proportion of non-netizens in rural areas is 4 1.2%. In terms of age, the elderly aged 60 and above are the main group of non-netizens. It can be seen that non-netizens are mainly distributed in rural areas, with the elderly aged 60 and above as the main group.
The lack of search behavior of this huge group of non-netizens leads to the fact that the search results that should have appeared are out of the sample, which leads to the underestimation of the search index of diseases such as "fever". According to the report of the US Centers for Disease Control and Prevention, the risk of severe coronavirus pneumonia-19 will increase with the increase of age, disability and basic diseases. In the late stage of Omicron, most hospital deaths occurred in adults over 65 years old and people with three or more basic diseases.
Figure: Coronavirus pneumonia-diagnosed every day in countries and regions around the world 19 cases.
Note: Due to limited testing, the number of confirmed cases is lower than the actual number of infected persons, and the data is as of June 65438+February 2, 20221.
▲ Data source: Johns Hopkins University CSSECOVID- 19 database, AI economic forecast of ourworldindata.org and Tengjing.
Figure: Coronavirus pneumonia-19 daily confirmed cases in various regions of the world.
Note: Due to limited testing, the number of confirmed cases is lower than the actual number of infected persons, and the data is as of June 65438+February 2, 20221.
▲ Data source: Johns Hopkins University CSSECOVID- 19 database, AI economic forecast of ourworldindata.org and Tengjing.
Big data is not perfect, why does Google flu trend fail?
As early as 1980, futurist alvin toffler toffler put forward the concept of "big data" in his book The Third Wave. Since ancient times, forecasting has always been a highly anticipated ability, and big data forecasting is the core application of data. Its logic is that every unconventional change must have signs beforehand and everything can be followed. If the law between signs and changes is found, it can be predicted.
There are precedents in the world for using big data methods and technologies for macroeconomic research and analysis. In the vision of big data analysis, we should not only understand the macro statistical laws, but also understand the fine structure in macro data. Based on the research perspective, the era of big data provides strong support for macroeconomic analysis and is changing the paradigm of macroeconomic research.
Central banks and other mainstream financial institutions develop and adopt real-time forecasting models to track the changes of economic conditions in real time and find reliable information sources before being overwhelmed by a large number of social information, so as to dynamically adjust the expectations of economic indicators. Including Nowcasting model of new york Fed, Wei model, GDPNow model of Atlanta Fed and MIDAS model of Bank of England.
According to Professor DidierSornette's "Dragon King" theory, there are two conditions for the occurrence of extreme events: the consistency and synergy of the system. When the consistency of the system is very strong, it is easy to have black swan-like extreme events. When the consistency and synergy of the system are strengthened at the same time, a more extreme "Dragon King" event beyond the "black swan" will occur.
"Black Swan" and "Dragon King" are not isolated events, but a series of strongly related events, which reflect the powerful role of positive feedback. When can the stock market be predicted? The key lies in the degree of correlation before and after the stock market changes.
Google launched the GoogleFluTrends system in 2008, with the motivation that it can detect disease activities early and respond quickly to reduce the impact of seasonal influenza and epidemic influenza. By analyzing a large number of Google search queries collected, we can reveal whether there are influenza-like diseases in the population. This logic and idea is actually very simple and intuitive-if you are sick, you are likely to search for information on search engines, such as how to treat it. Google decided to track these searches and use these data to try and predict the flu epidemic, even before medical institutions such as the Centers for Disease Control can do this.
In 2009, through the massive search data accumulated by Google, "Google Flu Trend" successfully predicted the spread of H 1N 1 flu in the United States, and became famous in World War I. It is reported that Google Flu Trend can predict the regional influenza epidemic 10 days before the US Centers for Disease Control and Prevention reports the influenza epidemic. The forecasting ability of GFT is obviously of great social significance, which can win an opportunity for the whole society to control the epidemic situation of infectious diseases in advance.
So Google created a strange equation on its website to calculate how many people are infected with the flu. The simple data logic is as follows: people's location+flu-related search queries on Google+some very clever algorithms = the number of flu patients in the United States.
The linear model is used to calculate the logarithmic probability of seeing a doctor for influenza-like diseases and the logarithmic probability of related search queries;
P is the percentage of doctor visits, and q is the query score related to ILI calculated in the previous step. β0 is the intercept, β 1 is the coefficient and ε is the error term.
The flu trend of Google has been proved to be not always accurate, especially during the period of 20 1 1 3, which overestimated the relative flu incidence, and the predicted visits during the flu season of 20 12 to 20 13 were twice as much as those recorded by the CDC. According to an article published in Nature on 20 13, Google Influenza Trend overestimated about 50% of influenza cases.
It can be seen that the application of big data to macroeconomic forecasting is not perfect. Economist and writer TimHarford believes that "the failure of Google's flu trend highlights the danger of unconstrained empiricism". One explanation for GFT's failure is that the news is full.
Figure: Comparison between Google ILI's estimate of influenza trend and CDC's estimate.
▲ Data source: Improving GoogleFlutrendEstimates for to Unets Troughransformation, Leah Martin, Biying Xu, Tengjing AI Economic Forecast.
In 20 13, Google adjusted its algorithm and responded that the "culprit" of the deviation was due to the extensive media coverage of GFT, which led to changes in people's search behavior. It seems that GFT did not consider introducing professional health care data and expert experience, nor did it "clean" and "denoise" user search data. After 20 1 1, Google launched "Recommended Related Search Words", which is the familiar search related word pattern today. The researchers analyzed that these adjustments may artificially push up some search indexes and lead to an overestimation of the incidence of the epidemic. For example, when users search for "fever", Google will also give relevant recommendations such as "sore throat and fever" and "how to treat sore throat". At this time, users may click because of curiosity and other reasons, resulting in the phenomenon that the keywords used by users are not intended by users, thus affecting the accuracy of GFT search data. Users' search behavior will in turn affect the prediction results of GFT. In the noisy world of search engines full of media reports and users' subjective information, there is also the paradox that "prediction is interference". There is a high probability that there will be a similar situation in the index of domestic search engines, which is our explanation based on GFT experience.
Figure: massive arithmetic "fever" related search words
▲ Data source: huge calculation, Tengjing AI economic forecast.
refer to
[1]CNNIC: 50th Statistical Report on Internet Development in China.
[2]
[3] Ajax, Hongke, Molinarin, et al. Mortalitryriskamongpatients was hospitalized in primarylyforcovid- 19, April 2020 _ June 2022, USA. MMWRMorbMortalWklyRep20227 1: 1 182_ 1 189.DOI:
[4]
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[6] D. Laaser, R. Kennedy, G. King, Anda. Vespignani.20 14。 "The Parapeloefgoogleflu: Trapsinbigdata analysis." Science 343: 1203_ 1205.
For more heavy research results, please pay attention to the official WeChat account Tengjing AI Economic Forecast.
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What month is the rainy season in Xi 'an in 2022?
Xi is a distinctive city. It has a variety of cultural heritage, as well as a variety of food and snacks, and is deeply loved by people. It's always raining in Xi 'an recently, and it's always rainy. This is a relatively normal phenomenon, mainly caused by subtropical high, global warming and geographical location.
202 1 why does Xi' an like rain in September?
1. Subtropical high
In September, it rained for more than ten days in Xi 'an. Judging from the meteorological data over the years, it is normal for Xi to have more rain in September. In fact, the possibility of the next ten days and a half months is relatively high.
Xi is a warm temperate semi-humid continental monsoon climate with moderate rainfall and four distinct seasons. Winter is cold, windy and foggy, with little rain and snow; Spring is warm, dry, windy and changeable; Summer is hot and rainy, summer drought is prominent, thunderstorms and strong winds; It is cool in autumn. The annual precipitation is 500 ~ 750 mm, mainly concentrated in summer and autumn; Xi is located in the northwest of subtropical high for a long time in summer and autumn, and southwest wind and northeast wind prevail in winter.
In winter, the subtropical high occupies the Pacific Ocean in the northern hemisphere. As the direct point of the sun moves northward, the subtropical high gradually moves northward. The northwest edge of subtropical high is easy to combine with cold air to form precipitation. However, due to the influence of topography, subtropical high intensity and other factors, the spring precipitation is mainly concentrated in East China and South China, which also leads to the precipitation peak in Xi 'an around May. In summer, Xi is controlled by subtropical high, and there are many short-term rainstorms. When autumn comes, the northwest edge of the subtropical high passes through Xi 'an again when it retreats southward, resulting in continuous precipitation in Xi 'an in September.
2. Global warming
The impact of global warming is complex. At present, the overall embodiment of rainfall is the northward movement of rainfall belt, but this northward movement is not just translation. Its scale and scope have local particularity. For example, with the global temperature rising gradually, the rainfall belt moved northward, and from the 1990s to the beginning of the new century, the precipitation in Shaanxi Province gradually decreased.
3. Geographical location
In fact, the Guanzhong basin where Xi 'an is located is not rich in water system, and the water area is relatively small, so it is difficult to form a large number of local thermal convection. In the south of the basin is the Qinling Mountains, which is the highest mountain range in the east. For Sichuan, the southwest airflow of the northwest Pacific subtropical high transports warm and humid air from the Indian Ocean to the Sichuan Basin, where it meets the cold air in the northern part of the Qinghai-Tibet Plateau, resulting in continuous autumn rains in western China in September and June at 5438+ 10. However, due to the existence of the Qinling Mountains, many warm and humid air currents form topographic precipitation during the climbing process on the south side of the Qinling Mountains, making it difficult to enter the Guanzhong Basin, which directly leads to two completely different dry and wet climates in Guanzhong and Hanzhong.
When is the rainy season in Xi 'an?
The rainy season in Xi is July, August and September. There are two obvious precipitation peaks in Xi, which are in July and September. Xi The average precipitation in Miko Wu is 558 ~ 750 mm, increasing gradually from north to south. It changes every year.
In September, the south of China, that is, the area near the Tropic of Cancer, was far from cooling down, and warm air was still hovering there, waiting for the cold current from the depths of Eurasia to drive them away.
Not only in southern China, but also in subtropical areas of South Asia and the Middle East, they are waiting for the same result. In addition, because the two subtropical highs are all along the coast, a lot of water vapor is also transpiration, but because of the hot weather, there is not much water vapor condensed into rain.
From September to June of 10, the subtropical high moved south and the rain belt returned to the west of China. It is said that there is rainy weather. This continuous autumn rain also has a scientific name, which is called "Autumn rain in western China" and "Autumn rain in Shaanxi". It is common in some areas of western China, and usually appears in Xi in September. Under the influence of the south subtropical high, the weather usually lasts about two to three weeks.
How can clothes dry faster in rainy days?
1. Paper towel press
After washing clothes, no matter how you twist them, there will always be a lot of water on them. You can iron clothes with paper towels. Paper towels are very absorbent. Take more paper towels to dry the water on your clothes.
Wring out a towel.
We use a dry towel to help wring it out. First, wrap the wet clothes with a dry towel, and then twist them hard. At this time, the water on the clothes will be absorbed by the towel. It is best to choose a towel with strong water absorption.
3. Add dry towels and shake well.
We can also dry it with a washing machine. We can dry it in the washing machine once, and then the second time.