From 6a29d1391949427b5e70735688c7187a5c61421f Mon Sep 17 00:00:00 2001 From: EduCoderStudent0014 Date: Fri, 25 Aug 2023 09:35:09 +0800 Subject: [PATCH] =?UTF-8?q?'=E6=8F=8F=E8=BF=B0'?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 210 ++- image/wps17.jpg | Bin 0 -> 31316 bytes image/wps18.jpg | Bin 0 -> 164648 bytes image/wps19.jpg | Bin 0 -> 204579 bytes image/wps20.jpg | Bin 0 -> 101160 bytes image/wps21.jpg | Bin 0 -> 134878 bytes image/wps22.jpg | Bin 0 -> 147042 bytes image/wps23.jpg | Bin 0 -> 200987 bytes image/wps24.jpg | Bin 0 -> 149864 bytes image/wps25.jpg | Bin 0 -> 134957 bytes image/wps26.jpg | Bin 0 -> 160144 bytes image/wps27.jpg | Bin 0 -> 26895 bytes image/wps28.jpg | Bin 0 -> 14877 bytes .../GetData.py | 126 ++ .../GetModel.py | 30 + .../Model.pkl | Bin 0 -> 1439292 bytes .../ProcessData.py | 160 ++ .../china_today.csv | 32 + .../date_test.csv | 15 + .../date_train.csv | 16 + .../date_valid.csv | 16 + .../main.py | 330 ++++ .../天气网.html | 1474 +++++++++++++++++ 23 files changed, 2408 insertions(+), 1 deletion(-) create mode 100644 image/wps17.jpg create mode 100644 image/wps18.jpg create mode 100644 image/wps19.jpg create mode 100644 image/wps20.jpg create mode 100644 image/wps21.jpg create mode 100644 image/wps22.jpg create mode 100644 image/wps23.jpg create mode 100644 image/wps24.jpg create mode 100644 image/wps25.jpg create mode 100644 image/wps26.jpg create mode 100644 image/wps27.jpg create mode 100644 image/wps28.jpg create mode 100644 基于Python的天气预测和可视化/GetData.py create mode 100644 基于Python的天气预测和可视化/GetModel.py create mode 100644 基于Python的天气预测和可视化/Model.pkl create mode 100644 基于Python的天气预测和可视化/ProcessData.py create mode 100644 基于Python的天气预测和可视化/china_today.csv create mode 100644 基于Python的天气预测和可视化/date_test.csv create mode 100644 基于Python的天气预测和可视化/date_train.csv create mode 100644 基于Python的天气预测和可视化/date_valid.csv create mode 100644 基于Python的天气预测和可视化/main.py create mode 100644 基于Python的天气预测和可视化/天气网.html diff --git a/README.md b/README.md index 3ba4253..54deddf 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,210 @@ -# weather-Forecast +# 一、项目介绍 +**项目名称:天气预测和天气可视化** + +天气预测和天气可视化是一个基于python机器学习(ml)的长春地区的天气预报项目,它实现了天气数据的爬取,预测和可视化。 + +项目结构如下: + +![img](image/wps26.jpg) + +* 天气数据的来源 + +GetData文件使用python爬虫技术,爬取长春和全国的天气信息数据 + +爬取网站:http://tianqi.2345.com/wea_history/54161.htm + +ProcessDate文件对爬取的天气数据进行了预处理 + +几个CSV文件保存的是爬取后并经过处理的数据 + + + +* 天气数据的预测 + +GetModel文件通过训练预测模型来预测长春近一周的天气,该文件利用Joblib将模型保存到本地 + +Main文件是项目主文件,通过运行该文件即可运行整个项目,该文件前部分获取保存到本地的预测模型来进行预测,并将预测结果打印到控制台 + + + +* 天气数据的可视化 + +Main文件后部分实现了天气数据的可视化 + + + +# 二、详细介绍 + +本项目分为三个部分,即爬取和处理数据,数据预测(包含评价方法)和数据可视化 + +## 1. 爬取和处理数据 + +数据爬取代码: + +````py +resq = requests.get(url, headers=headers, params=params) +data = resq.json()["data"] +# data frame +df = pd.read_html(data)[0] +```` + +即使用python爬取网站的json数据 + +### **数据预处理:** + +获取到的天气信息包括最高温,最低温都不是int格式的数字,通过对数据截取,将部分指标的数据变换为int类型 + +并对缺失值进行了处理 + +````py +my_imputer = SimpleImputer() +imputed_X_train = pd.DataFrame(my_imputer.fit_transform(X_train)) +imputed_X_valid = pd.DataFrame(my_imputer.transform(X_valid)) +```` + +通过SimpleImputer ,可以将现实数据中缺失的值通过同一列的均值、中值、或者众数补充起来,本项目使用了SimpleImputer的fit_transform对缺失值进行填充 + + + +## 2. 数据预测和模型评价方法 + +预测数据采用了机器学习算法——线性回归 +模型使用过程: + +### A. 提取数据 + +````py + 获取测试集、训练集、验证集 +[X_train, X_valid, y_train, y_valid, X_test] = ProcessData.ProcessData() +```` + +其中ProcessData()函数里使用了如下语句: + +````py +X_train, X_valid, y_train, y_valid = train_test_split(X, y, train_size=0.8, test_size=0.2, random_state=0) +```` + +train_test_split()是sklearn包的model_selection模块中提供的随机划分训练集和验证集的函数;使用train_test_split函数将完整的数据集和验证集以同等的比例分成2组不同的数据集和验证集 + +### B. 训练模型 + +选择了随机树森林模型(randomforest),然后用fit来训练模型 + +````py +# 随机树森林模型 +model = RandomForestRegressor(random_state=0, n_estimators=1001) +# 训练模型 +model.fit(X_train, y_train) +```` + +### C. 根据数据预测 + +````py +# 最终预测结果 +preds = model.predict(r[1]) +```` + +### D. **模型评价方法** + +````py +# 用MAE评估 +score = mean_absolute_error(y_valid, preds) +```` + +对于评估模型准确率的评价方法,本处使用的是MAE,也就是mean_absolute_error 平均绝对误差,就每个预测的数值离正确数值错误数值的平均数 + +假设: + +![img](image/wps27.jpg) + +平均绝对误差(Mean Absolute Error)等于: + +![img](image/wps28.jpg) + +范围[0,+∞),当预测值与真实值完全吻合时等于0,即完美模型;误差越大,该值越大。在本项目中: + +MAE: 3.8629148629148626 + +![img](image/wps17.jpg) + +## 3. **数据可视化** + +项目利用了pyecharts框架来实现绘图功能,实现天气的可视化 + +1. pyecharts是基于Echart图表的一个类库,而Echart是百度开源的一个可视化JavaScript库。 +2. pyecharts主要基于web浏览器进行显示,绘制的图形比较多,包括折线图、柱状图、饼图、漏斗图、地图、极坐标图等,代码量很少,而且很灵活,绘制出来的图形很美观。 + +导入相关的包如下: + +````py +from pyecharts.charts import Bar, Grid, Line, Tab +from pyecharts.components import Table +from pyecharts.options import ComponentTitleOpts +from pyecharts.charts import Map +from pyecharts import options as opts +```` + + + +# 三、项目展示 + +运行main.py,运行结果如下: + +![img](image/wps18.jpg) + +同时会生成一个html网页,名为天气网 + +![img](image/wps19.jpg) + + + +网页内容如下: + +## 1. 今日长春 + +首页今日长春以表格形式展现了长春当日的天气信息,包括日期,最高温,最低温,天气,风力风向,空气质量指数。 + +![img](image/wps20.jpg) + +## 2. 未来长春 + +以柱形图和折线图显示未来一星期长春天气的预测内容,包括最高温,最低温,空气质量指数。 + +![img](image/wps21.jpg) + +## 3. 近一周长春 + +以柱形图和折线图显示最近一星期长春的气温(最高温,最低温)和空气质量指数。 + +![img](image/wps22.jpg) + +## 4. 今日中国天气 + +以表格形式展现中国今日各个省的天气情况,实际显示的数据是该省的省会城市的天气信息。 + +![img](image/wps23.jpg) + +## 5. 今日全国空气质量 + +以地图形式展现中国今日各个省的的空气质量指数 + +![img](image/wps24.jpg) + +当鼠标移动到某个省上,会显示该省的空气质量: + +![img](image/wps25.jpg) + +# 四、存在的问题及未来改进的方向 + +* 存在的问题: + 1. 天气信息内容少,爬取数据只包含5个指标 + 2. 天气预测内容少,只对最高温,最低温,空气质量指标这三个数值指标进行预测,对如天气这类文字类型没有进行处理和预测 + 3. 预测模型准确率不够高,评估指标MEA为3.8629148629148626,还有提升空间,尽量减少预测值和实际值的误差 + 4. 网页不能进行交互,如切换城市 + +* 对应改进方向 + 1. 更换爬虫目标网站,获取更多天气信息指标,如降雨量 + 2. 增进预测内容,提高数据多元化 + 3. 可以改预测模型为RGBoost或用tensorflow来提高模型的准确率,从而降低MAE + 4. 可以结合html、js等前端技术来实现一个页面可以交互的网页 \ No newline at end of file diff --git a/image/wps17.jpg b/image/wps17.jpg new file mode 100644 index 0000000000000000000000000000000000000000..73155915429a2e69de298b88589e6346cdab99c9 GIT binary patch literal 31316 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a/基于Python的天气预测和可视化/GetData.py b/基于Python的天气预测和可视化/GetData.py new file mode 100644 index 0000000..850f1ab --- /dev/null +++ b/基于Python的天气预测和可视化/GetData.py @@ -0,0 +1,126 @@ +import requests +import pandas as pd +import datetime +''' + 使用python爬虫技术,爬取长春和全国的天气信息数据 + 爬取网站:http://tianqi.2345.com/wea_history/54161.htm + areaid 和各省会城市对应关系 + area_id = [ + ("黑龙江", 50953), ("内蒙古", 53463),("吉林", 54161), ("辽宁", 54342), + ("河北", 53698), ("天津", 54527), ("山西", 53772), ("陕西",57036 ), + ("甘肃",52889 ), ("宁夏",53614 ), ("青海",52866 ), ("新疆", 51463), + ("西藏", 55591), ("四川", 56294), ("重庆", 57516), ("山东", 54823), + ("河南", 57083), ("江苏",58238 ), ("安徽", 58321), ("湖北", 57494), + ("浙江", 58457), ("福建",58847 ), ("江西", 58606), ("湖南",57687 ), + ("贵州",57816 ), ("广西", 59431), ("海南",59758 ), ("上海",58362 ), + ("广东",59287), ("云南",56778), ("台湾",59554) , +] + +''' + +# 提供年份和月份,爬取对应的的表格数据 +url = "http://tianqi.2345.com/Pc/GetHistory" +headers = { + "User-Agent": + """Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/101.0.4951.41 Safari/537.36 Edg/101.0.1210.32""" +} + +def craw_table(id,year,month): + params = { + "areaInfo[areaId]": id, + "areaInfo[areaType]": 2, + "date[year]": year, + "date[month]": month + } + resq = requests.get(url, headers=headers, params=params) + data = resq.json()["data"] + # data frame + df = pd.read_html(data)[0] + return df + + +# 输入城市id,爬取该城市今日的天气数据 +def getToday(id): + # 获取当前年份和月份 + today = datetime.datetime.today() + year = today.year + month = today.month + # 获取当日长春天气数据 + month_data =craw_table(id, year, month) + return month_data.tail(1) + +# 输入城市id,爬取该城市近七周的天气数据 +def getWeek(id): + # 获取当前年份和月份 + today = datetime.datetime.today() + year = today.year + month = today.month + # 获取当日长春天气数据 + month_data =craw_table(id, year, month) + return month_data.tail(7) + +# 爬取全国各个省会城市的今日的天气数据 +def getChinaToday(): + ids=[50953, 53463,54161,54342,53698,54527,53772,57036 ,52889,53614,52866,51463, + 55591, 56294, 57516,54823,57083,58238, 58321, 57494, 58457,58847,58606, + 57687,57816 ,59431,59758 ,58362 ,59287,56778,59554] + list=[] + for i in ids: + df=getToday(i) + list.append(df) + return pd.concat(list).reset_index(drop=True) + +# 获取长春最近3年的天气数据,用于预测 +def getYears(): + today = datetime.datetime.today() + df_list = [] + for year in range(today.year-5, today.year): + for month in range(1, 13): + df = craw_table(54161,year, month) + df_list.append(df) + + for month in range(1,today.month+1): + df = craw_table(54161, today.year, month) + df_list.append(df) + # 多年数据合并 + return pd.concat(df_list).reset_index(drop=True) + +# 传入一个时间范围,获取某个时间范围的天气数据 +def getPredictDate(year0,month0,day0,year1,month1,day1): + id=54161 + date_list=[] + if month0!=month1: + date0=craw_table(id,year0,month0) + date_ago=date0[day0-1:] + date1 = craw_table(id,year1, month1) + date_pre = date1[:day1] + + date_list.append(date_ago) + date_list.append(date_pre) + date=pd.concat(date_list).reset_index(drop=True) + else: + date0 = craw_table(id, year0, month0) + date=date0[day0-1:day1] + return date + + + +''' +def craw_year(year1, year2): + df_list= [] + for year in range(year1, year2): + for month in range(1, 13): + df =craw_table(year, month) + df_list.append(df) + # 多年数据合并 + return pd.concat(df_list).reset_index(drop=True) + + #df =craw_table(2022,4) +#print(df) +#date =df["最高温"] +#print(date) +''' + + + + diff --git a/基于Python的天气预测和可视化/GetModel.py b/基于Python的天气预测和可视化/GetModel.py new file mode 100644 index 0000000..b4f03ac --- /dev/null +++ b/基于Python的天气预测和可视化/GetModel.py @@ -0,0 +1,30 @@ + +from sklearn.ensemble import RandomForestRegressor +import joblib +from sklearn.metrics import mean_absolute_error +import ProcessData + + +# 训练并保存模型 +def getModel(a="Model.pkl"): + """ + :param a: 模型文件名 + :return: + [socre: MAE评估结果, + X_test: 预测数据集] + """ + # 获取测试集、训练集、验证集 + [X_train, X_valid, y_train, y_valid, X_test] = ProcessData.ProcessData() + + # 随机树森林模型 + model = RandomForestRegressor(random_state=0, 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ProcessData.setAir(week_data) +low_temperature = ProcessData.setLowTemp(week_data) +high_temperature = ProcessData.setHighTemp(week_data) + +def grid_week() -> Grid: + x_data = ["前七天", "前六天", "前五天", "前四天", "前三天", "前两天", "前一天"] + bar = ( + Bar() + .add_xaxis(x_data) + .add_yaxis( + "最高温", + high_temperature, + yaxis_index=0, + color="#d14a61", + ) + .add_yaxis( + "最低温", + low_temperature, + yaxis_index=1, + color="#5793f3", + ) + .extend_axis( + yaxis=opts.AxisOpts( + name="最高温", + type_="value", + min_=-30, + max_=40, + position="right", + axisline_opts=opts.AxisLineOpts( + linestyle_opts=opts.LineStyleOpts(color="#d14a61") + ), + axislabel_opts=opts.LabelOpts(formatter="{value} °C"), + ) + ) + .extend_axis( + yaxis=opts.AxisOpts( + type_="value", + name="天气质量指数", + min_=0, + max_=300, + position="left", + axisline_opts=opts.AxisLineOpts( + linestyle_opts=opts.LineStyleOpts(color="#675bba") + ), + axislabel_opts=opts.LabelOpts(formatter="{value}"), + splitline_opts=opts.SplitLineOpts( + is_show=True, linestyle_opts=opts.LineStyleOpts(opacity=1) + ), + ) + ) + .set_global_opts( + yaxis_opts=opts.AxisOpts( + name="最低温", + min_=-30, + max_=40, + position="right", + offset=80, + axisline_opts=opts.AxisLineOpts( + linestyle_opts=opts.LineStyleOpts(color="#5793f3") + ), + axislabel_opts=opts.LabelOpts(formatter="{value} °C"), + ), + title_opts=opts.TitleOpts(title=""), + tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"), + ) + ) + + line = ( + Line() + .add_xaxis(x_data) + .add_yaxis( + "天气质量指数 " + "优(0~50) 良(51~100) 轻度(101~150) 中度(151~200) 重度(201~300)", + airs, + yaxis_index=2, + color="#675bba", + label_opts=opts.LabelOpts(is_show=False), + ) + ) + + bar.overlap(line) + return Grid().add( + bar, opts.GridOpts(pos_left="5%", pos_right="20%"), is_control_axis_index=True + ) + +''' +可视化预测长春的天气 +''' + +# 预测长春一周的天气和空气 +predict_airs=[] +predict_low_temperature=[] +predict_high_temperature=[] +x_data=[] +for i in range(0,7): + predict_high_temperature.append(round(preds[i][0],4)) + predict_low_temperature.append(round(preds[i][1],4)) + predict_airs.append(round(preds[i][2],4)) + x_data.append((today + DT.timedelta(days=i)).date()) + +def grid_week_predict() -> Grid: + bar = ( + Bar() + .add_xaxis(x_data) + .add_yaxis( + "最高温", + predict_high_temperature, + yaxis_index=0, + color="#d14a61", + ) + .add_yaxis( + "最低温", + predict_low_temperature, + yaxis_index=1, + color="#5793f3", + ) + .extend_axis( + yaxis=opts.AxisOpts( + name="最高温", + type_="value", + min_=-30, + max_=40, + position="right", + axisline_opts=opts.AxisLineOpts( + linestyle_opts=opts.LineStyleOpts(color="#d14a61") + ), + axislabel_opts=opts.LabelOpts(formatter="{value} °C"), + ) + ) + .extend_axis( + yaxis=opts.AxisOpts( + type_="value", + name="天气质量指数", + min_=0, + max_=300, + position="left", + axisline_opts=opts.AxisLineOpts( + linestyle_opts=opts.LineStyleOpts(color="#675bba") + ), + axislabel_opts=opts.LabelOpts(formatter="{value}"), + splitline_opts=opts.SplitLineOpts( + is_show=True, linestyle_opts=opts.LineStyleOpts(opacity=1) + ), + ) + ) + .set_global_opts( + yaxis_opts=opts.AxisOpts( + name="最低温", + min_=-30, + max_=40, + position="right", + offset=80, + axisline_opts=opts.AxisLineOpts( + linestyle_opts=opts.LineStyleOpts(color="#5793f3") + ), + axislabel_opts=opts.LabelOpts(formatter="{value} °C"), + ), + title_opts=opts.TitleOpts(title=""), + tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"), + ) + ) + + line = ( + Line() + .add_xaxis(x_data) + .add_yaxis( + "天气质量指数 " + "优(0~50) 良(51~100) 轻度(101~150) 中度(151~200) 重度(201~300)", + predict_airs, + yaxis_index=2, + color="#675bba", + label_opts=opts.LabelOpts(is_show=False), + ) + ) + + bar.overlap(line) + return Grid().add( + bar, opts.GridOpts(pos_left="5%", pos_right="20%"), is_control_axis_index=True + ) + + + + +''' +获取全国各省会城市今日的天气情况 +''' +china_today = GetData.getChinaToday() +china_today.to_csv("china_today.csv") + + +def setData(str,i): + return china_today[i:i+1][str].values[0] +provinces = [ + "黑龙江","内蒙古", "吉林", "辽宁", "河北","天津","山西", "陕西", + "甘肃","宁夏", "青海","新疆", "西藏", "四川", "重庆", "山东", "河南", + "江苏", "安徽","湖北", "浙江", "福建", "江西", "湖南", "贵州", + "广西", "海南","上海","广东","云南","台湾" +] +rows=[] +for i in range(0,31): + rows.append([provinces[i],setData('最低温',i),setData('最高温',i),setData('天气',i),setData('风力风向',i)]) + + +def today_china_table() ->Table: + c=( + Table() + .add(["省份","最低温","最高温", "天气", "风力风向"], rows) + .set_global_opts( + title_opts=ComponentTitleOpts(title="今日全国各省会城市的天气信息表", subtitle="") + ) + ) + return c + + +china_airs = ProcessData.setAir(china_today) +airs_list=[] +for i in range(0,31): + airs_list.append(china_airs[i]) + +def today_china() ->Map: + c = ( + Map() + .add("天气质量指数 优(0~50) 良(51~100) 轻度(101~150) 中度(151~200) 重度(201~300)", [list(z) for z in zip(provinces, airs_list)], "china") + .set_global_opts( + title_opts=opts.TitleOpts(title="今日中国空气质量"), + visualmap_opts=opts.VisualMapOpts(max_=300), + ) + ) + return c + + +# 分页图的标题 +tab = Tab() +tab.add(table_main(), "今日长春") +tab.add(grid_week_predict(), "未来长春") +tab.add(grid_week(), "近一周长春") +tab.add(today_china_table(), "今日中国天气") +tab.add(today_china(), "今日全国空气质量") +tab.render("天气网.html") + + +''' + + all_high_t = [] + all_low_t = [] + all_air = [] + all_high_t.append(preds[a][0]) + all_low_t.append(preds[a][1]) + all_air.append(preds[a][2]) +temp = {"最高温": all_high_t, "最低温": all_low_t, "空气质量": all_air} +# 绘画折线图 +plt.plot(range(1, 7), temp["最高温"], color="red", label="high_t") +plt.plot(range(1, 7), temp["最低温"], color="blue", label="low_t") +plt.legend() # 显示图例 +plt.ylabel("Temperature(°C)") +plt.xlabel("day") +# 显示 +plt.show() +plt.plot(range(1, 7), temp["空气质量"], color="black", label="air") +plt.legend() +plt.ylabel(" ") +plt.xlabel("day") +plt.show() +''' \ No newline at end of file diff --git a/基于Python的天气预测和可视化/天气网.html b/基于Python的天气预测和可视化/天气网.html new file mode 100644 index 0000000..cc273ac --- /dev/null +++ b/基于Python的天气预测和可视化/天气网.html @@ -0,0 +1,1474 @@ + + + + + Awesome-pyecharts + + + + + + + +

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    日期最高温最低温天气风力风向空气质量指数
    2022-05-10 周二24°多云~小雨西南风4级46 优
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    今日全国各省会城市的天气信息表

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    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    省份最低温最高温天气风力风向
    黑龙江25°多云~阵雨西南风4级
    内蒙古16°小雨~晴西北风3级
    吉林24°多云~小雨西南风4级
    辽宁10°22°多云~阵雨西南风3级
    河北12°13°雾~阴东南风2级
    天津12°16°多云~阴南风2级
    山西10°22°多云~阴西南风2级
    陕西15°23°多云~阴东北风1级
    甘肃13°22°多云~浮尘东北风3级
    宁夏10°25°多云东风3级
    青海17°多云~阴东风2级
    新疆14°27°多云~晴北风2级
    西藏20°多云~阵雨西南风2级
    四川18°29°多云~中雨南风2级
    重庆20°28°多云~阴东风2级
    山东14°19°雾~阴西南风2级
    河南12°21°晴~多云南风2级
    江苏16°17°雾~阴东北风3级
    安徽15°19°多云~阴东风2级
    湖北15°19°雾~阴东北风3级
    浙江16°20°小雨~阴东风1级
    福建20°21°小雨东北风1级
    江西18°19°中雨~阴北风3级
    湖南15°20°中雨~阴西北风3级
    贵州14°24°多云~阵雨东北风2级
    广西22°27°小雨~中雨东南风2级
    海南25°31°多云东南风3级
    上海18°21°多云~阴东南风3级
    广东23°27°小雨~暴雨东南风3级
    云南15°25°多云西南风2级
    台湾24°28°阴~小雨西南风2级
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