我撰写关于气候变化和气候科学家的报道已有多年，我一直对科学家与公众之间存在的脱节感到沮丧，这种情况我屡见不鲜。科学家和研究人员经常在全球平均气温比工业化前水平上升超过 2 摄氏度（3.6 华氏度）将引发的风险这一背景下讨论全球变暖。撇开科学家不谈，普通人该如何理解这一点？依我经验，作用不大。
I have been writing about climate change and climate scientists for years, and I have been frustrated by a disconnect between scientists and the public that I’ve seen time and again. Scientists and researchers often discuss global warming in the context of risks that will arise if the planet’s average global temperature rises more than 2 degrees Celsius (3.6 degrees Fahrenheit) above pre-industrial levels. Scientists aside, what is an ordinary person to make of that? Not much, in my experience.
另一个令我沮丧的地方是：预测如果我们无法找到办法阻止地球气温升过这一阈值将面临的严重后果，此类报道不胜枚举。但眼下呢？已经发生了什么？为什么这些严峻警告往往只能引来耸肩？
Another frustration: There is no shortage of stories predicting the dire consequences we face if we don’t find a way to stop the planet’s temperature from rising beyond that threshold. But what about now? What has already happened? And why do these dire warnings so often land with a shrug?
于是我自问：“大多数人是如何与天气和气温打交道的？”他们早上醒来会想今天会有多热、多冷。没人会问自己：“今天的平均气温会是多少？”当他们在夏季热浪的第七天醒来时，可能会呻吟着问：“这什么时候是个头？”他们可能还会感叹，今年已经发生了好几轮这样的热浪，比他们记忆中成长时经历的要多。
So I asked myself, “How do most people interact with weather and temperatures?”They get up in the morning and wonder how hot and how cold it will be. No one asks themselves, “What will the average temperature be today?”And when they wake up on the seventh day of a summer heat wave, they might groan and ask, “Will this ever end?”They might also lament that there have been several such heat waves this year, more than they remember growing up.
在我看来，热浪是全球变暖科学与我们日常体验之间的连接纽带。热浪是人们已经理解的现象，我以为或许可以借此解释近几十年地球变暖了多少。
Heat waves, it seemed to me, are the connective tissue between the science of global warming and our everyday experience. Heat waves are something people already understand, and I thought perhaps they could help explain how much the planet has warmed in recent decades.
遗憾的是，并不存在一个公认的热浪定义。因此，在咨询了气候研究人员后，我们路透社的几位同事着手建立自己的定义。数据记者本·韦尔什、查尔斯·西曼斯基和我于 2024 年开始认真思考这个想法，今年韦尔什推出了“路透气候监测”，追踪全球每日异常气温。
Unfortunately, no universally accepted definition of a heat wave existed. So, in consultation with climate researchers, a few of us at Reuters set out to build our own. Data journalists Ben Welsh, Charles Szymanski and I started seriously thinking about this idea in 2024, and this year Welsh rolled out the Reuters Climate Monitor, which tracks daily anomalous temperatures worldwide.
在“气候监测器”的底层，是我们开发的一套更具雄心的概念与方法体系，用于定义、追踪和绘制热浪图谱。热浪变得更加频繁、范围更大，这并未让我感到意外——但我们的数据分析揭示的结果却令人震惊且不祥。
Under the hood of the Climate Monitor, however, was a more ambitious system of concepts and methods we developed to define, track and map heat waves. The fact that heat waves have become more frequent and larger didn’t surprise me – but what our data analysis turned up is breathtaking and ominous.
我们发现的一点是，我们需要一个新术语来描述最大规模的热浪，一个简单且能瞬间传达其规模与后果的词汇。因此，我们创设了一个新类别——“热风暴”，定义为峰值时覆盖地球表面至少 1% 面积的热浪，约相当于加拿大面积的一半。一些科学家对该术语提出异议，因为“风暴”在气象学上有明确定义。但它也有通俗的隐喻含义——根据《韦氏词典》，“任何自然要素的严重扰动”——我们认为这契合这些热浪事件。
One thing we discovered is that we needed a new term for the largest heat waves, something simple that would instantly convey their scale and consequences. So we created a new category, the “heat storm” — defined as heat waves that at their peak cover at least 1% of the planet, about half the size of Canada. Some scientists objected to the term, because “storm” has a distinct meteorological definition. But it also has a common metaphorical meaning — “a serious disturbance of any element of nature,” per Merriam-Webster — that we believe fits these heat events.
我们系列报道中的故事（可在此阅读）以及我们的热风暴追踪系统，均建立在下述方法与定义之上。我们还将热浪追踪方法开源，以便其他人能使用统一定义开展自己的热浪研究。相关内容可在我们的 GitHub 页面找到。
The stories in our series, which you can read here, and our heat-storm tracking system rest on the methods and definitions below. We have also open-sourced our heat wave-tracking approach so others can conduct their own research on heat waves using a uniform definition. It can be found on our GitHub page.
我们为此项目咨询了二十余位研究人员和科学家。其中四位在我们制定方法论时影响尤为显著：澳大利亚国家科学机构 CSIRO 的 Alistair Hobday，因其在海洋热浪方面的研究；汉堡大学的 Lukas Brunner，协助我们进行季节性调整和基准温度范围设定；巴黎美国大学的 Claudio Piani，同样协助我们建立历史温度范围；以及雷丁大学的 Ed Hawkins，他让我们确信这并非徒劳无功。
We consulted two dozen researchers and scientists for this project. Four of them were particularly influential as we developed our methodology. Alistair Hobday, of CSIRO, of Australia’s National Science Agency, for his work on marine heat waves; Lukas Brunner, at the University of Hamburg, who helped with our seasonal adjustments and baseline temperature ranges; Claudio Piani at the American University of Paris, who also helped us develop our historical temperature ranges; and Ed Hawkins at the University of Reading, who convinced us that this was not a fool’s errand.
下方关于来源、定义和方法论的详细章节面向气候与天气专家，部分内容较为技术化。对于普通读者，以下是核心要点：ERA5数据集由欧洲中期天气预报中心（ECMWF）制作，时间跨度为1961年1月1日至1990年12月31日，采用UTC时间格式，每天记录24小时内的气温数据。该数据集整合了多种气候数据来源，包括气象站、卫星、气象气球以及船舶观测数据，并将这些数据输入到模型中，以估算覆盖全球的104万个网格点的气温值。因此，ERA5被广泛认为是全球气候数据的“黄金标准”。
The detailed section below on Sources, Definitions and Methodology is aimed at climate and weather specialists, and it’s technical in places. For general readers, here are the essentials: ERA5, 0.25 degrees, European Centre for Medium-Range Weather Forecasts (ECMWF). This dataset incorporates a variety of climate data sources, including weather stations, satellites, weather balloons, and ship readings. It then feeds the data into a model that estimates temperature values for 1.04 million grid squares covering the planet. It is widely considered the gold standard of global climate data. (For full citation, please see below.) 1/1/1961-12/31/1990, using UTC dates with 24 hourly temperatures each day.
该数据集提供了地球上每个网格点在地面或水面以上两米高度处的平均日最高气温和最低气温的估算值。其中，高温阈值对应第90百分位数，低温阈值对应第10百分位数。
The estimated average daily high and low air temperatures at two meters above the ground or water surface for each of the grid squares on the planet. High temperature threshold: 90th percentile. Low temperature threshold: 10th percentile.
“平滑处理”技术的目的是为了减少某一天内出现的异常高温或低温对30年基准期数据的影响。具体而言，路透社使用的基准温度分布包含930个数据点：即该日期的实际最高/最低气温，以及该日期前后15天的气温数据。通过这些数据，我们计算出第90百分位数和第10百分位数作为温度阈值。在非闰年中，我们通过平均2月28日和3月1日的温度来生成2月29日的合成数据。
“Smoothing” aims to reduce the effect of one or two unusually hot or cold days over the 30-year baseline for any given calendar day. So, for each calendar date, the Reuters baseline temperature distribution consists of 930 values: the corresponding high/low observations from that date and the 15 calendar days before and after it across the 30-year baseline period. We calculate the 90th and 10th percentile thresholds from those values. In non-leap years, we generate synthetic Feb. 29 data by averaging Feb. 28 and March 1.
由于季节性变化可能会影响31天时间窗口内的温度分布，我们采用了汉堡大学的Lukas Brunner和维也纳大学的Aiko Voigt在2024年发表于《Nature Communications》杂志上的论文中提出的方法来调整数据。
Because seasonal changes can creep into the edges of our 31-day window and skew the temperature distribution, we adjust our data using the method outlined in “Pitfalls in diagnosing temperature extremes,” a 2024 Nature Communications paper by Lukas Brunner of the University of Hamburg and Aiko Voigt of the University of Vienna.
通过从每个日历日期的31天平均值中减去对应的基准值，然后重新计算分布。我们使用该分布，称之为基准范围或历史范围，以确定定义极端情况的百分位阈值。Brunner 审查了实施季节性调整技术的计算机代码。
This is done by subtracting each calendar date’s 31-day average from the corresponding baseline value and then recalculating the distribution. We use that distribution, which we call the baseline range or historical range, to establish the percentile thresholds that define what qualifies as extreme. Brunner vetted the computer code implementing the seasonal adjustment technique.
0.25度网格覆盖面积约为770平方公里（28公里×28公里）在热带地区，540平方公里（28公里×19公里）在中纬度地区，260平方公里（28公里×9公里）在极地地区。
A 0.25-degree grid covers about 770 km² (28 km × 28 km) in the tropics, 540 km² (28 km × 19 km) in the mid-latitudes, and 260 km² (28 km × 9 km) in the polar regions.
5,100,720平方公里 90百分位数表示偏离历史常态的偏差，90%的天数会低于该阈值——即该网格方格中温度异常高于该日期常态的温度。10百分位数表示偏离常态的偏差，90%的天数会高于该阈值——即该日期温度异常低于该常态。我们从季节性调整后的基准温度中计算这些阈值。我们依据已发表的研究和与气候及海洋热浪研究人员的协商，选择了90百分位数和10百分位数。
5,100,720 km² The 90th percentile represents the deviation from the historical norm that 9 out of 10 days would fall below – the threshold beyond which a day’s temperature is unusually warm for that calendar date in that grid square. The 10th percentile represents the deviation from the norm that 9 out of 10 days would exceed – the threshold below which a day’s temperature is unusually cool. We calculate these thresholds from seasonally adjusted baseline temperatures. We chose the 90th and 10th percentiles based on published research and consultations with climate and marine heat wave researchers.
当网格单元记录到五天加权平均百分位排名达到或超过90百分位时，我们识别出热浪。我们这样做是为了防止任何单日略低于90百分位的情况干扰整体明显的趋势。加权平均值基于滚动5天窗口，采用指数衰减（因子0.9）。衰减倾向于更近期的温度，因此第5天温度高于90百分位的情况，有可能超过第1天或第2天温度略低于90百分位的情况。
We identify a heat wave when a grid cell records five days with a weighted-average percentile rank at or above the 90th percentile. We did this so that slight dips below the 90th percentile on any given day don’t override the broader, obvious trend. The weighted average is calculated over a rolling 5-day window with exponential decay (factor 0.9). The decay favors temperatures on more recent days, so temperatures above the 90th percentile on day five can sometimes overcome temperatures slightly below the 90th percentile on day one or two, for example.
我们采用了一整天的宽限期，意味着一个网格单元可以在一天内未达到资格而不终止连续性。一旦网格单元的宽限日被重置，该单元可以在该单元重新融合的第二天重新应用。一旦达到五个符合条件的天数，热浪即保持活跃，只要加权平均百分位排名保持在90百分位或以上，同时适用相同的宽限期。
We apply a one-day grace period throughout, meaning a cell may fail to qualify for a single day without ending the streak. Once the grace day for a grid cell is reset, it can be reapplied the day after the cell is reintegrated. Once five qualifying days are reached, the heat wave remains active as long as the weighted average percentile rank stays at or above the 90th percentile, subject to the same grace period.
路透社的技术基于2016年由CSIRO及其合作者Alistair Hobday撰写的论文“分层方法定义海洋热浪”所确立的定义进行调整。
The Reuters technique adapts the definition of marine heat waves established in the 2016 paper "A hierarchical approach to defining marine heat waves" by Alistair Hobday of CSIRO and his coauthors.
当一个网格单元连续五天的加权平均百分位排名处于10百分位或以下时，即识别出一次寒潮。加权平均是基于滚动的5天窗口计算的，采用指数衰减（因子0.9）。整个过程中应用了一天的宽限期，意味着一个单元可以在一天内未达到资格而不终止连续性。一旦达到五个符合条件的天数，寒潮即保持活跃，只要加权平均百分位排名保持在10百分位或以下，同时适用相同的宽限期。
A cold snap is identified when a grid cell records five days with a weighted average percentile rank at or below the 10th percentile. The weighted average is calculated over a rolling 5-day window with exponential decay (factor 0.9). A one-day grace period is applied throughout, meaning a cell may fail to qualify for a single day without ending the streak. Once five qualifying days are reached, the cold snap remains active as long as the weighted average percentile rank stays at or below the 10th percentile, subject to the same grace period.
进入热浪或寒潮状态后，网格单元可以在该单元跌至/升至90百分位/10百分位后的一天内保持该状态，但次日若重新跌至/升至90百分位/10百分位以下，则可继续保持状态。宽限日承认了局部温度在一天内出现小幅波动可能会打破热浪的连续性，从而让亲历者产生争议。我们在计算热浪或热浪风暴的面积时不使用宽限日单元，在判断某地在任意一天是否位于热浪或热浪风暴内时也忽略它们。
After entering into a heat wave or cold snap state, a grid cell can remain in that state if the cell drops below/above the 90th/10th percentile for one day but rises above/below the 90th/10th percentile the following day. The grace day acknowledges that small shifts in local temperatures for one day could break the continuity of a heat wave in a way that anyone living through it might dispute. We do not use grace day cells when calculating the area of a heat wave or heat storm, and we ignore them when deciding whether a place is inside a heat wave or heat storm on any given day.
热浪和寒潮有三种几何形态。它们分别是极端天气事件的实际几何形态，即基于处于热浪或寒潮状态的网格单元的范围；缓冲几何形态，用于将彼此极其接近但不相邻的网格单元合并为一个共同的温度事件；以及宽限期几何形态，用于追踪宽限期天数。
There are three geometries for heat waves and cold snaps. They are the actual geometry of the extreme weather event, which is the extent based on the grid squares in a state of heat wave or cold snap; the buffer geometry, which is used to join grid squares that are extremely close to each other but don’t touch into a common temperature event; and the grace geometries, which are used to track grace days.
通常，我们将所有处于热浪或寒潮状态且共享边的网格单元合并为一个统一的几何形态。如果两个不同区域仅被一个网格单元隔开，我们将它们视为同一热浪或寒潮事件。事件在首次符合条件的那天（异常高温或低温的第五天）被宣布，并被分配一个唯一的ID。
Generally, we merge all grid cells that are in the state of a heat wave or cold snap and that share an edge into a unified geometry. If two distinct areas are separated by just one grid square, we consider them the same heat wave or cold snap event. The event is declared on the first day it qualifies (the fifth day of abnormal heat or cold) and is assigned a unique ID.
热暴或寒暴是指其几何形态覆盖面积达到地球表面积至少1%的热浪或寒潮，约为510万平方公里。这一阈值在一定程度上是任意设定的，但旨在捕捉非常大且罕见的热浪和寒潮事件。
A heat storm or cold storm is a heat wave or cold snap whose geometry reaches at least 1% of Earth’s area, approximately 5.1 million km². This threshold was somewhat arbitrary, but is meant to capture very large and rare heat wave and cold snap events.
如果今天的缓冲几何形态与前一天的缓冲几何形态重叠面积达到前者的至少60%，它将继承该风暴的ID。如果它与前一天的多个缓冲几何形态在60%的阈值上均有重叠，它将继承重叠面积最大的那个缓冲几何形态的风暴ID。如果不满足60%的阈值，它将生成一个新的风暴ID。随着时间的推移，这一过程可能导致风暴分裂、合并或新生。名称在几何形态首次达到地球总表面积1%的那天被分配。
If a buffer geometry from today overlaps at least 60% of a buffer geometry from the previous day, it inherits that storm ID. If it overlaps several buffer geometries from the previous day at the 60% threshold, it will inherit the storm ID of the buffer geometry with the largest area of overlap. If it does not meet the 60% threshold, it will generate a new storm ID. Over time, this process can cause storms to split, merge, or be created. A name is assigned on the first day the geometry reaches 1% of Earth’s total surface area.
我们统计了每个国家中按性别划分的最受欢迎的名字，根据排名对这些名字进行排序，并按大洲进行汇总；这样，在同一区域内多个国家中都常见的名字就会获得更高的评分。为了保持名称的字母平衡性，我们将这些名字分成了长度为13个字母的组，然后按照大洲和性别进行排序，以确保全球范围内的代表性。我们剔除了重复的名字、长度少于三个字母的名字，以及那些含义模糊或不合适的英文名字。
Usingwe pulled the most popular names by gender for each country, scored them by rank and aggregated them by continent so names common across multiple countries within a region score higher. We grouped them into 13-letter buckets for alphabetic balance, then organized them by continent and gender to ensure global representation. We removed obvious duplicates, names with fewer than three letters, and names with ambiguous or inappropriate English meanings.
从最初的4000个名字中，最终筛选出了2380个合适的名字。我们使用一种算法为每场风暴命名，该算法确保风暴的名字来自不同的字母表区域和不同的地区，并且男女名字交替使用。每个风暴的名字都会与其发生的年份结合在一起，形成最终的标识符。我们的名字数据库可以在这里查看。
From an initial list of 4,000 names, we ended up with 2,380. An algorithm names each storm using a formula that ensures storms get names from different parts of the alphabet and different world regions, with alternating genders. The name is paired with the storm’s start year to create the final identifier. Our name database is available here.
我们构建了一个3×3的网格，以城市中心所在的网格点为中心。如果某个城市位于该网格的范围内，那么这个城市就被认为处于热浪、寒潮或风暴的影响范围内。在热带地区，这个范围通常是距离城市59公里以内；在中纬度地区是51公里以内；在极地地区则是44公里以内。在极少数情况下，一个城市可能会同时受到两种或更多种天气事件的影响。
We build a 3-by-3 grid centered on the grid square where the city center lies. If any part of that grid is within the actual geometry, the city is considered to be within the heat wave, cold snap or storm. In the tropics, it typically means the area must be within about 59 km of the city; in the mid-latitudes, about 51 km; and in the polar regions, about 44 km. In rare cases, this method may place a city in two or more temperature events at the same time.
在这种情况下，如果存在被命名的热浪，我们会将该城市归类到该热浪事件中；否则，我们会将其归类到全球范围内最早发生的天气事件中。无论有多少个天气事件同时发生，每个日历日期都只被计算一次。
关键规则：
In such cases, we assign the city-day to the named heat storm if one exists; otherwise, we assign it to the event that started earliest globally. We count each calendar day only once, regardless of how many events overlap.
- 必须在活动开始前的连续五天内，相关天气现象（如海浪或风暴）的强度值处于第90百分位或第10百分位以上；因此，一个城市或特定地区的“热浪”或“寒潮”天数是从热浪/寒潮开始日期前四天开始计算的。换句话说，这些天数是从温度首次出现异常情况，随后演变成热浪、寒潮或风暴的那一刻开始计算的。如果某个温度事件紧接另一个温度事件之后发生，导致两个事件在时间上重叠，那么这四天的时间会被归入较大的那个事件中（例如：如果一个是热浪，另一个是风暴，那么这四天的时间会被视为热浪的相关天数）。
Given that waves and storms must have been above or below the 90th or 10th percentile for five days before the start of the event, days that a city or other specific location has been in a heat wave are counted starting four days before the start date of the heat wave or cold snap. In other words, days are counted from when temperatures first become anomalous and later become a heat wave, cold snap or a storm. If a temperature event immediately precedes another temperature event, causing the events to overlap in days, we assign the four lead-in days to the larger event. So, if one event is a heat wave and the other is a heat storm, the lead-in days are categorized as heat storm days.
- 温度数据是通过一个3×3的网格来采集的，但只有位于该事件影响范围内的网格单元才会被用于计算平均值。
Temperatures are taken using the 3-by-3 grid, but only the grid cells inside the event are used and then averaged.
- 对于地球上的某个具体地点，我们会计算该地点在3×3网格所覆盖的9个网格单元内的历史平均温度、第90百分位温度以及第10百分位温度。
For a specific place or spot on the planet, we calculate the average for the historical norm, the 90th percentile temperature and the 10th percentile temperature across the nine grid squares that comprise the 3-by-3 grid.
- 根据某天的温度超出第90百分位阈值的程度，这些网格单元会被划分为不同的强度等级。等级的确定依据是实际温度与阈值之间的差距（即温度异常值与阈值之间的差值）。具体来说：
Grid squares are assigned an intensity level based on how much any day’s temperature exceeds the 90th percentile threshold. The level is determined by the ratio of the actual anomaly to the threshold gap.
如果基准温度（历史平均值）为25℃，第90百分位温度为30℃，那么阈值差距为5℃；
如果某天的最高温度为30℃（比基准温度高5℃），则属于第1级；
So if the baseline – or historical norm – is 25 C and the 90th percentile is 30 C, the threshold gap is 5 degrees. (For readers accustomed to Fahrenheit, 30 C is 86 F.) A high of 30 C would be 5 degrees above baseline, making it a level 1 day.
如果最高温度为35℃（比基准温度高10℃，即阈值差距的两倍），则属于第2级；
A high of 35 C would be 10 degrees above baseline, or twice the threshold gap, making it a level 2 day.
如果最高温度为40℃（比基准温度高15℃，即阈值差距的三倍），则属于第3级；依此类推。
A high of 40 C (15 degrees above baseline, three times the gap) would be level 3; 45 C (20 degrees above baseline, four times the gap) would be level 4, and so on.
- 相邻的、属于同一强度等级的网格单元会被合并，以反映该强度等级在该天气事件中的实际影响范围。
Contiguous grid cells reaching the same intensity level are aggregated to create a geometry for that level within the storm.
我们将“级联现象”（cascade）定义为连续发生或同时发生的热浪事件，且这些热浪在地球上的持续时间是不间断的。所谓“级联现象”的持续时间，指的是至少有一场热浪在地球表面持续活跃的天数。
We define a cascade as heat storms that run consecutively or simultaneously for an uninterrupted period of time. The duration of a cascade is the number of days that at least one heat storm was active on the planet.
欧洲中期天气预报中心（ECMWF）其整个天气预报系统都是基于一个网格化的数据集构建的；该数据集的分辨率取决于每个网格单元的大小——极地地区的网格单元面积为260平方公里，而热带地区的网格单元面积为770平方公里。需要说明的是，数据集中的温度估计值并非实际观测结果，而是根据观测数据通过模型计算得出的。
The ECMWF bases its entire system on a gridded dataset that is only as granular as each grid cell, which range in area from 260 km² in the polar regions to 770 km² in the tropics. The dataset’s temperature estimates are not actual observations but are modeled from observations, as the ECMWF explains here.
在地形复杂的区域（如高海拔地区或沿海地区），模型的预测结果可能与实际观测值存在显著差异。以纽约市为例：该市的部分区域濒临大西洋，因此被水域覆盖的网格单元与完全被陆地覆盖的网格单元之间的温差可能达到数度。路透社通过使用以某个特定地点为中心的3×3网格来尽量减少这些预测误差；同时，他们还会对数据结果进行季节性调整，以更准确地反映气候变化趋势。
The models can diverge notably from observations in complex terrain, such as high altitudes and coastal areas. For example, parts of New York City touch the Atlantic Ocean, and the temperature difference between a grid square partially covered by water and land and a grid square fully over land can be several degrees. Using a 3-by-3 grid around a particular place is one way Reuters attempts to minimize those limitations. Seasonal adjustment is another. Copernicus Climate Change Service, Climate Data Store (2023).
数据来源：Copernicus气候变化服务（Climate Change Service）的气候数据存储库（Climate Data Store），数据涵盖1940年至今的每小时天气数据（ERA5格式）。欧洲委员会（European Commission）及欧洲中期天气预报中心（ECMWF）对用户如何使用这些数据概不负责。关于更多详细信息，请点击此处查看相关文档。
ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). Generated using Copernicus Climate Change Service information. Neither the European Commission nor the European Centre for Medium-Range Weather Forecasts (ECMWF) is responsible for any use that may be made of the Copernicus information or data it contains. For full documentation, click here.
城市位置数据来源于Esri的“世界城市数据库”（World Cities database）；路透社已将菲律宾的塔克洛班市（Tacloban City）添加到了数据集中。
Cities location: Taken from Esri’s World Cities database. Reuters added Tacloban City, Philippines, to the dataset.
国家及地区的边界信息则来源于Natural Earth数据集。
Country and territory boundaries: Taken from the Natural Earth dataset. (