MATLAB算法判别热带气旋

将六条要求合并到一起，做成一个条件嵌套的判别式，能够一层一层往下，符合第一个要求，就判断下一个。最好附带一个台风实例（任意都行），验证该台风是否符合要求，能否通过判别。

% 1.计算相对涡度
max_vorticity_850 = max(vorticity_850, [], 'all') % 计算相对涡度
critical_vorticity = 8*10^-5;
if max_vorticity_850 < critical_vorticity
is_typhoon = false;
end
 
% 2.计算850hPa的最大风速
max_wind_speed_850 = max(wind_speed_850, [], 'all'); % 计算最大风速
max_wind_speed_300 = max(wind_speed_300, [], 'all'); % 计算最大风速
critical_wind_speed = 13;
if max_wind_speed_850 < critical_wind_speed
is_typhoon = false;
end

% 3.计算暖芯
[~, max_vorticity_idx] = max(vorticity, [], 'all', 'linear'); % 最大相对涡度的索引
[lat_idx, lon_idx] = ind2sub(size(vorticity), max_vorticity_idx); % 最大相对涡度的经纬度索引
lat_box = lat(lat_idx-5:lat_idx+4, lon_idx-5:lon_idx+4); % 选取10°x 10°的经纬度格网
lon_box = lon(lat_idx-5:lat_idx+4, lon_idx-5:lon_idx+4);
t300_box = t300(lat_idx-5:lat_idx+4, lon_idx-5:lon_idx+4); % 选取相应区域内的300,500和700 hPa的温度
t500_box = t500(lat_idx-5:lat_idx+4, lon_idx-5:lon_idx+4);
t700_box = t700(lat_idx-5:lat_idx+4, lon_idx-5:lon_idx+4);
mean_t300_box = mean(t300_box, 'all'); % 计算均值
mean_t500_box = mean(t500_box, 'all');
mean_t700_box = mean(t700_box, 'all');
max_t_box = max([t300_box(:); t500_box(:); t700_box(:)]); % 计算最大温度
temp_deviation = [max_t_box - mean_t300_box, max_t_box - mean_t500_box, max_t_box - mean_t700_box]; % 计算温度偏差
sum_temp_deviation = sum(temp_deviation);
critical_deviation = 0.8;
if sum_temp_deviation < critical_deviation
is_typhoon = false;
end
 
% 4.比较850hPa和300hPa的最大风速
max_wind_speed_300 = max(wind_speed_300, [], 'all'); % 计算最大风速
if max_wind_speed_850 < max_wind_speed_300
is_typhoon = false;
end

% 5.在北印度洋应用半径阈值
if in_nio(lon_box, lat_box)
radius_threshold = 200e3; % 200 km 半径阈值
max_wind_speed_location = max(wind_speed_850, [], 'all', 'linear'); % 最大风速的经纬度索引
[storm_lat_idx, storm_lon_idx] = ind2sub(size(wind_speed_850), max_wind_speed_location); % 最大风速的经纬度
[storm_lat, storm_lon] = deal(lat(storm_lat_idx), lon(storm_lon_idx)); % 最大风速的经纬度值
% 计算最大平均风速的半径
[R, ~, ~] = distance(storm_lat, storm_lon, lat_box, lon_box, referenceSphere('Earth', 'meters'));
radius = mean(R(wind_speed_850 > 0.8*max_wind_speed_850));
% 如果最大平均风速的半径大于半径阈值，则认为不是台风
if radius > radius_threshold
is_typhoon = false;
end
end
 
% 6.计算台风的持续时间
time_threshold = 36*3600; % 36小时
typhoon_duration = time(end) - time(1); % 台风持续时间
if typhoon_duration < time_threshold
is_typhoon = false;
end

clc
close all
clera all
% 1.计算相对涡度
max_vorticity_850 = max(vorticity_850, [], 'all'); % 计算相对涡度
critical_vorticity = 8*10^-5;
if max_vorticity_850 < critical_vorticity
    is_typhoon = false;
else
    % 2.计算850hPa的最大风速
    max_wind_speed_850 = max(wind_speed_850, [], 'all'); % 计算最大风速
    max_wind_speed_300 = max(wind_speed_300, [], 'all'); % 计算最大风速
    critical_wind_speed = 13;
    if max_wind_speed_850 < critical_wind_speed
        is_typhoon = false;
    else
        
        % 3.计算暖芯
        [~, max_vorticity_idx] = max(vorticity, [], 'all', 'linear'); % 最大相对涡度的索引
        [lat_idx, lon_idx] = ind2sub(size(vorticity), max_vorticity_idx); % 最大相对涡度的经纬度索引
        lat_box = lat(lat_idx-5:lat_idx+4, lon_idx-5:lon_idx+4); % 选取10°x 10°的经纬度格网
        lon_box = lon(lat_idx-5:lat_idx+4, lon_idx-5:lon_idx+4);
        t300_box = t300(lat_idx-5:lat_idx+4, lon_idx-5:lon_idx+4); % 选取相应区域内的300,500和700 hPa的温度
        t500_box = t500(lat_idx-5:lat_idx+4, lon_idx-5:lon_idx+4);
        t700_box = t700(lat_idx-5:lat_idx+4, lon_idx-5:lon_idx+4);
        mean_t300_box = mean(t300_box, 'all'); % 计算均值
        mean_t500_box = mean(t500_box, 'all');
        mean_t700_box = mean(t700_box, 'all');
        max_t_box = max([t300_box(:); t500_box(:); t700_box(:)]); % 计算最大温度
        temp_deviation = [max_t_box - mean_t300_box, max_t_box - mean_t500_box, max_t_box - mean_t700_box]; % 计算温度偏差
        sum_temp_deviation = sum(temp_deviation);
        critical_deviation = 0.8;
        if sum_temp_deviation < critical_deviation
            is_typhoon = false;
        else
            
            % 4.比较850hPa和300hPa的最大风速
            max_wind_speed_300 = max(wind_speed_300, [], 'all'); % 计算最大风速
            if max_wind_speed_850 < max_wind_speed_300
                is_typhoon = false;
            else
                
                % 5.在北印度洋应用半径阈值
                if in_nio(lon_box, lat_box)
                    radius_threshold = 200e3; % 200 km 半径阈值
                    max_wind_speed_location = max(wind_speed_850, [], 'all', 'linear'); % 最大风速的经纬度索引
                    [storm_lat_idx, storm_lon_idx] = ind2sub(size(wind_speed_850), max_wind_speed_location); % 最大风速的经纬度
                    [storm_lat, storm_lon] = deal(lat(storm_lat_idx), lon(storm_lon_idx)); % 最大风速的经纬度值
                    % 计算最大平均风速的半径
                    [R, ~, ~] = distance(storm_lat, storm_lon, lat_box, lon_box, referenceSphere('Earth', 'meters'));
                    radius = mean(R(wind_speed_850 > 0.8*max_wind_speed_850));
                    % 如果最大平均风速的半径大于半径阈值，则认为不是台风
                    if radius > radius_threshold
                        is_typhoon = false;
                    else
                        % 6.计算台风的持续时间
                        time_threshold = 36*3600; % 36小时
                        typhoon_duration = time(end) - time(1); % 台风持续时间
                        if typhoon_duration < time_threshold
                            is_typhoon = false;
                        end
                    end
                else
                    % 6.计算台风的持续时间
                    time_threshold = 36*3600; % 36小时
                    typhoon_duration = time(end) - time(1); % 台风持续时间
                    if typhoon_duration < time_threshold
                        is_typhoon = false;
                    end
                end
            end
        end
    end
end

还请提供一下数据

Matlab实现热带气旋不同风期的风速转换

%%
clc;
clear;

%%
fullpath = mfilename('fullpath');
[path, name] = fileparts(fullpath);

xlsxfile = strcat(path, '/ibtracs.since1980.list.v04r00.xlsx');

%%
[A, T, R] = xlsread(xlsxfile);

%%
SID = R(3 : size(R, 1), 1);
A = A(2 : size(A, 1), :);
WMO_WIND = A(:, 10);
LANDFALL = A(:, 15);
WMO_AGENCY = R(3 : size(R, 1), 13);

USA_WIND = A(:, 23); % 1-minute wind speeds
TOKYO_WIND = A(:, 45); % Maximum sustained wind speed [10-min averaging period]
CMA_WIND = A(:, 57); % Two-minute mean maximum sustained wind
HKO_WIND = A(:, 62);
NEWDELHI_WIND = A(:, 67); % 3-minute
REUNION_WIND = A(:, 75); % 10 minute
BOM_WIND = A(:, 95); % 10-minute
NADI_WIND = A(:, 120); % 10 minute
WELLINGTON_WIND = A(:, 124); % 10-minute
DS824_WIND = A(:, 129);
TD9636_WIND = A(:, 134);
TD9635_WIND = A(:, 138);
NEUMANN_WIND = A(:, 144);
MLC_WIND = A(:, 149);

STORM_SPEED = A(:, 161);
STORM_DIR = A(:, 162);

%%
% in land, V10 / V1 = 1 / 1.21  |  at sea, V10 / V1 = 1 / 1.05
% in land, V10 / V2 = 1 / 1.12  |  at sea, V10 / V2 = 1 / 1.02
% in land, V10 / V3 = 1 / 1.09  |  at sea, V10 / V3 = 1 / 1.00

USA_WIND10 = USA_WIND - USA_WIND; 
CMA_WIND10 = CMA_WIND - CMA_WIND; 
NEWDELHI_WIND10 = NEWDELHI_WIND - NEWDELHI_WIND;

idx_sea = find(LANDFALL > 0);
idx_land = find(LANDFALL == 0);

% at sea
USA_WIND10(idx_sea) = USA_WIND(idx_sea) / 1.05; % 1min to 10min
CMA_WIND10(idx_sea) = CMA_WIND(idx_sea) / 1.02; % 2min to 10min
NEWDELHI_WIND10(idx_sea) = NEWDELHI_WIND(idx_sea) / 1.00; % 3min to 10min

% in land
USA_WIND10(idx_land) = USA_WIND(idx_land) / 1.21; % 1min to 10min
CMA_WIND10(idx_land) = CMA_WIND(idx_land) / 1.12; % 2min to 10min
NEWDELHI_WIND10(idx_land) = NEWDELHI_WIND(idx_land) / 1.09; % 3min to 10min

%%
uniAGENCY = unique(WMO_AGENCY);
codeAGENCY = zeros(size(WMO_AGENCY));
% codeAGENCY: USA_WIND10            idx_atcf -> 1 | idx_cphc -> 1 | idx_hurdat_atl -> 1 | idx_hurdat_epa -> 1
% codeAGENCY: BOM_WIND              idx_bom -> 2
% codeAGENCY: NADI_WIND             idx_nadi -> 3
% codeAGENCY: NEWDELHI_WIND10       idx_newdelhi -> 4
% codeAGENCY: REUNION_WIND          idx_reunion -> 5
% codeAGENCY: TOKYO_WIND            idx_tokyo -> 6  
% codeAGENCY: WELLINGTON_WIND       idx_wellington -> 7

idx_atcf = find(strcmp(WMO_AGENCY, uniAGENCY(2)));
idx_bom = find(strcmp(WMO_AGENCY, uniAGENCY(3)));
idx_cphc = find(strcmp(WMO_AGENCY, uniAGENCY(4)));
idx_hurdat_atl = find(strcmp(WMO_AGENCY, uniAGENCY(5)));
idx_hurdat_epa = find(strcmp(WMO_AGENCY, uniAGENCY(6)));
idx_nadi = find(strcmp(WMO_AGENCY, uniAGENCY(7)));
idx_newdelhi = find(strcmp(WMO_AGENCY, uniAGENCY(8)));
idx_reunion = find(strcmp(WMO_AGENCY, uniAGENCY(9)));
idx_tokyo = find(strcmp(WMO_AGENCY, uniAGENCY(10)));
idx_wellington = find(strcmp(WMO_AGENCY, uniAGENCY(11)));

codeAGENCY([idx_atcf; idx_cphc; idx_hurdat_atl; idx_hurdat_epa]) = 1;
codeAGENCY(idx_bom) = 2;
codeAGENCY(idx_nadi) = 3;
codeAGENCY(idx_newdelhi) = 4;
codeAGENCY(idx_reunion) = 5;
codeAGENCY(idx_tokyo) = 6;
codeAGENCY(idx_wellington) = 7;

%%
for codi = 1 : 7
    idx_curcode = find(codeAGENCY == codi);
    curSID = unique(SID(idx_curcode));
    for curSIDi = 1 : size(curSID, 1)
        [codi curSIDi size(curSID, 1)]
        codeAGENCY(find(strcmp(SID, curSID(curSIDi)))) = codi;
    end
end

%%
Vmax_10min = zeros(size(codeAGENCY));
Vmax_10min(find(codeAGENCY == 1)) = USA_WIND10(find(codeAGENCY == 1));
Vmax_10min(find(codeAGENCY == 2)) = BOM_WIND(find(codeAGENCY == 2));
Vmax_10min(find(codeAGENCY == 3)) = NADI_WIND(find(codeAGENCY == 3));
Vmax_10min(find(codeAGENCY == 4)) = NEWDELHI_WIND10(find(codeAGENCY == 4));
Vmax_10min(find(codeAGENCY == 5)) = REUNION_WIND(find(codeAGENCY == 5));
Vmax_10min(find(codeAGENCY == 6)) = TOKYO_WIND(find(codeAGENCY == 6));
Vmax_10min(find(codeAGENCY == 7)) = WELLINGTON_WIND(find(codeAGENCY == 7));

%%
% There are the values of codeAGENCY with 0
% Vmax_10min(codeAGENCY == 0) = mean(Vmax1, Vmax2, ..., VmaxN that have transed to V_10min)
all_Vs = [TOKYO_WIND, REUNION_WIND, BOM_WIND, NADI_WIND, WELLINGTON_WIND, USA_WIND10, CMA_WIND10, NEWDELHI_WIND10];
all_V10min = mean(all_Vs, 2, 'omitnan');
% 
% OUT = [V10min, STORM_SPEED, STORM_DIR];
outVmax_10min = Vmax_10min;
outVmax_10min(find(codeAGENCY == 0)) = all_V10min(find(codeAGENCY == 0));
outVmax_10min(find(outVmax_10min == 0)) = -1;
outVmax_10min(isnan(outVmax_10min)) = -1;

%%
outpath = strcat(path, '\adj_V10min.since1980.xlsx');
xlswrite(outpath, num2cell(outVmax_10min));

以下内容由CHATGPT及阿里嘎多学长共同生成、有用望采纳：

这个算法是用来判别热带气旋的，将六条要求合并到一起，做成一个条件嵌套的判别式。其中包括计算相对涡度、计算850hPa的最大风速、计算暖芯、比较850hPa和300hPa的最大风速、在北印度洋应用半径阈值以及计算台风的持续时间等步骤。以下是一个基于这个算法的简单示例：

% 加载数据文件
load('typhoon_data.mat');

% 计算相对涡度
max_vorticity_850 = max(vorticity_850, [], 'all');
critical_vorticity = 8*10^-5;
if max_vorticity_850 < critical_vorticity
    is_typhoon = false;
end

% 计算850hPa的最大风速
max_wind_speed_850 = max(wind_speed_850, [], 'all');
max_wind_speed_300 = max(wind_speed_300, [], 'all');
critical_wind_speed = 13;
if max_wind_speed_850 < critical_wind_speed
    is_typhoon = false;
end

% 计算暖芯
[~, max_vorticity_idx] = max(vorticity, [], 'all', 'linear');
[lat_idx, lon_idx] = ind2sub(size(vorticity), max_vorticity_idx);
lat_box = lat(lat_idx-5:lat_idx+4, lon_idx-5:lon_idx+4);
lon_box = lon(lat_idx-5:lat_idx+4, lon_idx-5:lon_idx+4);
t300_box = t300(lat_idx-5:lat_idx+4, lon_idx-5:lon_idx+4);
t500_box = t500(lat_idx-5:lat_idx+4, lon_idx-5:lon_idx+4);
t700_box = t700(lat_idx-5:lat_idx+4, lon_idx-5:lon_idx+4);
mean_t300_box = mean(t300_box, 'all');
mean_t500_box = mean(t500_box, 'all');
mean_t700_box = mean(t700_box, 'all');
max_t_box = max([t300_box(:); t500_box(:); t700_box(:)]);
temp_deviation = [max_t_box - mean_t300_box, max_t_box - mean_t500_box, max_t_box - mean_t700_box];
sum_temp_deviation = sum(temp_deviation);
critical_deviation = 0.8;
if sum_temp_deviation < critical_deviation
    is_typhoon = false;
end

% 比较850hPa和300hPa的最大风速
max_wind_speed_300 = max(wind_speed_300, [], 'all');
if max_wind_speed_850 < max_wind_speed_300
    is_typhoon = false;
end

% 在北印度洋应用半径阈值
if in_nio(lon_box, lat_box)
    radius_threshold = 200e3;
    max_wind_speed_location = max(wind_speed_850, [], 'all', 'linear');
    [storm_lat_idx, storm_lon_idx] = ind2sub(size(wind_speed_850), max_wind_speed_location);
    [storm_lat, storm_lon] = deal(lat(storm_lat_idx), lon(storm_lon_idx));
    [R, ~, ~] = distance(storm_lat, storm_lon, lat_box, lon_box, referenceSphere('Earth', 'meters'));
    radius = mean(R(wind_speed_850 > 0.8*max_wind_speed_850));
    if radius > radius_threshold
        is_typhoon = false;
    end
end

% 计算台风的持续时间
time_threshold = 36*3600;
typhoon_duration = time(end) - time(1);
if typhoon_duration < time_threshold
    is_typhoon = false;
end

% 打印结果
if is_typhoon
    disp("这是一场热带气旋");
else
    disp("这不是一场热带气旋");
end

这个示例代码中，我们首先加载了一个名为 typhoon_data.mat 的数据文件，其中包含了一些与热带气旋相关的数据，包括相对涡度、风速、温度等等。然后，我们根据这个算法的六个要求，依次进行判断。如果判断结果为 false，则说明这不是一场热带气旋；否则，说明这是一场热带气旋。最后，我们打印出了判别结果。

• 帮你找了个相似的问题, 你可以看下: https://ask.csdn.net/questions/7672244
• 这篇博客也不错, 你可以看下matlab频域滤波处理，手动滤波，图像去网格背景：频域中手动选择目标区域进行滤波，滤掉噪声，灵活地达到理想的效果。
• 您还可以看一下 硬核野生技术咨询客服小李老师的matlab零基础入门路径规划城市遍历机器人路径等问题课程中的 变量类型小节, 巩固相关知识点
  

以下答案由GPT-3.5大模型与博主波罗歌共同编写：
这里通过使用一个布尔型变量 is_typhoon的方式，同时满足六个要求才能够被视为一次台风。在每个要求的判断中，只要不符合台风的特征，就将 is_typhoon 设置为 false。最后，只有 is_typhoon 仍为 true，才会确认这是一个台风。

下面是代码示例：

% 计算相对涡度
max_vorticity_850 = max(vorticity_850, [], 'all'); % 计算相对涡度
critical_vorticity = 8*10^-5;
if max_vorticity_850 < critical_vorticity
    is_typhoon = false;
end

% 计算850hPa的最大风速
max_wind_speed_850 = max(wind_speed_850, [], 'all'); % 计算最大风速
max_wind_speed_300 = max(wind_speed_300, [], 'all'); % 计算最大风速
critical_wind_speed = 13;
if max_wind_speed_850 < critical_wind_speed
    is_typhoon = false;
end

% 计算暖芯
[~, max_vorticity_idx] = max(vorticity, [], 'all', 'linear'); % 最大相对涡度的索引
[lat_idx, lon_idx] = ind2sub(size(vorticity), max_vorticity_idx); % 最大相对涡度的经纬度索引
lat_box = lat(lat_idx-5:lat_idx+4, lon_idx-5:lon_idx+4); % 选取10°x 10°的经纬度格网
lon_box = lon(lat_idx-5:lat_idx+4, lon_idx-5:lon_idx+4);
t300_box = t300(lat_idx-5:lat_idx+4, lon_idx-5:lon_idx+4); % 选取相应区域内的300,500和700 hPa的温度
t500_box = t500(lat_idx-5:lat_idx+4, lon_idx-5:lon_idx+4);
t700_box = t700(lat_idx-5:lat_idx+4, lon_idx-5:lon_idx+4);
mean_t300_box = mean(t300_box, 'all'); % 计算均值
mean_t500_box = mean(t500_box, 'all');
mean_t700_box = mean(t700_box, 'all');
max_t_box = max([t300_box(:); t500_box(:); t700_box(:)]); % 计算最大温度
temp_deviation = [max_t_box - mean_t300_box, max_t_box - mean_t500_box, max_t_box - mean_t700_box]; % 计算温度偏差
sum_temp_deviation = sum(temp_deviation);
critical_deviation = 0.8;
if sum_temp_deviation < critical_deviation
    is_typhoon = false;
end

% 比较850hPa和300hPa的最大风速
max_wind_speed_300 = max(wind_speed_300, [], 'all'); % 计算最大风速
if max_wind_speed_850 < max_wind_speed_300
    is_typhoon = false;
end

% 在北印度洋应用半径阈值
if in_nio(lon_box, lat_box)
    radius_threshold = 200e3; % 200 km 半径阈值
    max_wind_speed_location = max(wind_speed_850, [], 'all', 'linear'); % 最大风速的经纬度索引
    [storm_lat_idx, storm_lon_idx] = ind2sub(size(wind_speed_850), max_wind_speed_location); % 最大风速的经纬度
    [storm_lat, storm_lon] = deal(lat(storm_lat_idx), lon(storm_lon_idx)); % 最大风速的经纬度值
    % 计算最大平均风速的半径
    [R, ~, ~] = distance(storm_lat, storm_lon, lat_box, lon_box, referenceSphere('Earth', 'meters'));
    radius = mean(R(wind_speed_850 > 0.8*max_wind_speed_850));
    % 如果最大平均风速的半径大于半径阈值，则认为不是台风
    if radius > radius_threshold
        is_typhoon = false;
    end
end

% 计算台风的持续时间
time_threshold = 36*3600; % 36小时
typhoon_duration = time(end) - time(1); % 台风持续时间
if typhoon_duration < time_threshold
    is_typhoon = false;
end

% 最终判断
if is_typhoon
    disp('该气旋是台风');
else
    disp('该气旋不是台风');
end

注意，这里为了代码的可读性，将每个要求都写成了一个代码块。在实际应用中，可以将这些判断合并到一起，形成一个条件嵌套的判别式。
如果我的回答解决了您的问题，请采纳！