MATLAB探测热带气旋的算法

编写一段MATLAB算法
要求如下

850 hPa的最大相对涡度超过(8.0 ×10-5)s-1。
850 hPa最大风速超过13.0 m s-1。
高空有一个明显温暖的地核。也就是说，300、500和700 hPa的温度偏差总和超过(0.8、2.0和0.5)k。每个水平的温度偏差是通过从最靠近最大850 hPa涡度位置的10° × 10°网格上的平均温度中减去最高温度来计算的。
850 hPa的最大风速大于300 hPa的最大风速。
为了消除北印度洋的热带低气压，最大平均风速的半径必须小于距离探测到的风暴中心200公里。此条件仅适用于NIO。
每次探测到的风暴持续时间必须超过36小时。为了防止在单个6小时时间步长内检测和终止产生双重热带气旋计数，允许在单个时间步长内终止。

需要在同一时刻满足所有条件。例如有24小时，探测每一个时刻是否都符合这些条件，将满足条件的所有时刻选取出来，并展现出它所在的经纬度地点，并用线段连接起来。

利用ERA5 2022年每小时压力水平数据。

你现在有没有 ERA5 2022年每小时压力水平数据 的文件 , 可以给我发一份吗 ? 我本地实现一下再给你, 原谅我不想去ECMFW下载了

可以试试可采纳

读取输入文件，获取所需变量的数据。
对于每个时刻，检查是否符合所有条件。如果符合，则记录该时刻的位置信息。
选取所有满足条件的时刻的位置信息，用线段连接起来并显示在地图上。
代码实现如下：

% 读取输入文件
filename = 'data.nc';
lat = ncread(filename, 'lat'); % 纬度数据
lon = ncread(filename, 'lon'); % 经度数据
time = ncread(filename, 'time'); % 时间数据
u850 = ncread(filename, 'u850'); % 850 hPa的u分量风速数据
v850 = ncread(filename, 'v850'); % 850 hPa的v分量风速数据
vort850 = ncread(filename, 'vort850'); % 850 hPa的相对涡度数据
u300 = ncread(filename, 'u300'); % 300 hPa的u分量风速数据
v300 = ncread(filename, 'v300'); % 300 hPa的v分量风速数据
u500 = ncread(filename, 'u500'); % 500 hPa的u分量风速数据
v500 = ncread(filename, 'v500'); % 500 hPa的v分量风速数据
u700 = ncread(filename, 'u700'); % 700 hPa的u分量风速数据
v700 = ncread(filename, 'v700'); % 700 hPa的v分量风速数据
t300 = ncread(filename, 't300'); % 300 hPa的温度数据
t500 = ncread(filename, 't500'); % 500 hPa的温度数据
t700 = ncread(filename, 't700'); % 700 hPa的温度数据

% 设置条件阈值
max_vort850 = 8.0e-5; % 最大相对涡度
max_wind850 = 13.0; % 850 hPa最大风速
sum_t = 0.8 + 2.0 + 0.5; % 高空温暖的地核
max_wind850_300 = true; % 850 hPa的最大风速大于300 hPa的最大风速
max_radius = 200; % 最大平均风速半径，仅适用于北印度洋
duration = 36; % 最小持续时间，单位：小时

% 初始化位置信息
locations = [];
% 遍历每个时刻
for iTime = 1:length(time)
    % 获取当前时刻的各种数据
    curr_u850 = squeeze(u850(:,:,iTime));
    curr_v850 = squeeze(v850(:,:,iTime));
    curr_vort850 = squeeze(vort850(:,:,iTime));
    curr_u300 = squeeze(u300(:,:,iTime));
    curr_v300 = squeeze(v300(:,:,iTime));
    curr_u500 = squeeze(u500(:,:,iTime));
    curr_v500 = squeeze(v500(:,:,iTime));
    curr_u700 = squeeze(u700(:,:,iTime));
    curr_v700 = squeeze(v700(:,:,iTime));
    curr_t300 = squeeze(t300(:,:,iTime));
    curr_t500 = squeeze(t500(:,:,iTime));
    curr_t700 = squeeze(t700(:,:,iTime));
    
    % 检查是否符合条件
    [is_valid, lat_max_vort, lon_max_vort] = check_conditions(curr_u850, curr_v850, curr_vort850, curr_u300, curr_v300, curr_u500, curr_v500, curr_u700, curr_v700, curr_t300, curr_t500, curr_t700, max_vort850, max_wind850, sum_t, max_wind850_300, max_radius, duration);
    if is_valid
        locations = [locations; [lat_max_vort, lon_max_vort]]; % 记录位置信息
    end
end

% 连接满足条件的位置信息，并绘制在地图上
plot(locations(:,2), locations(:,1), '-o'); % 纬度在前，经度在后，画线段连接点，并用圆圈标记每个点

仅供参考：
假设已经有了提取并处理好的气象数据，存储在变量data中，每一行对应一个时间步长，每一列对应一个气象参数，包括经度、纬度、时间、850 hPa的相对涡度、850 hPa最大风速、300、500和700 hPa的平均温度等
该代码逐一检查每个时间步长是否符合上述条件，并将符合条件的风暴的经纬度坐标、时间、气象参数等信息存储在一个结构体数组storms_found中，其中每个元素对应一个找到的风暴。最后，利用MATLAB内置的绘图函数绘制所有找到的风暴的轨迹。

% 设置条件阈值
vort_threshold = 8.0e-5;
wind_threshold = 13.0;
temp_threshold = [0.8, 2.0, 0.5]; % 对应着300、500和700 hPa
radius_threshold = 200; % 热带低气压的半径小于200公里
min_duration = 36; % 最少持续36小时

% 初始化结果变量
storms_found = {};
i_storm = 0;

for i_time = 1:size(data, 1)
    % 检查条件1
    if ~any(data(i_time, 4) > vort_threshold)
        continue;
    end
    
    % 检查条件2
    if ~any(data(i_time, 5) > wind_threshold)
        continue;
    end
    
    % 找到850 hPa的最大相对涡度的位置
    [~, i_vort_max] = max(data(i_time, 4));
    lon_vort_max = data(i_time, 1 + i_vort_max);
    lat_vort_max = data(i_time, 2 + i_vort_max);
    
    % 计算温度偏差
    temp_devs = zeros(1, 3);
    for i_lvl = 1:3
        i_temp = find(data(i_time, 3 + i_lvl) == i_lvl * 200 + 100); % 找到对应层次的气温数据
        if ~isempty(i_temp)
            lon_temp = data(i_time, 1 + i_temp);
            lat_temp = data(i_time, 2 + i_temp);
            temp_devs(i_lvl) = mean(data(i_time, 6 + i_lvl) - data(i_time, 6 + i_lvl - 1)) - ...
                (data(i_time, 6 + i_temp) - mean(data(i_time, 6 + i_lvl - 1))); % 计算温度偏差
        end
    end
    
    % 检查条件3
    if sum(temp_devs) < temp_threshold(1) + temp_threshold(2) + temp_threshold(3)
        continue;
    end
    
    % 找到850 hPa和300 hPa最大风速的位置
    [~, i_wspd_850_max] = max(data(i_time, 5));
    [~, i_wspd_300_max] = max(data(i_time, 6));
    lon_wspd_850_max = data(i_time, 1 + i_wspd_850_max);
    lat_wspd_850_max = data(i_time, 2 + i_wspd_850_max);
    lon_wspd_300_max = data(i_time, 1 + i_wspd_300_max);
    lat_wspd_300_max = data(i_time, 2 + i_wspd_300_max);
    
    % 检查条件4
    if data(i_time, 5 + i_wspd_850_max) <= data(i_time, 6 + i_wspd_300_max)
        continue;
    end
    
    % 针对北印度洋，找到和最大风速相应的位置
    if lon_vort_max > 40 && lon_vort_max < 110 && lat_vort_max > -5 && lat_vort_max < 35
        i_wspd_max = i_wspd_850_max; % 850 hPa最大风速满足条件
    else
        i_wspd_max = find(sqrt((data(i_time, 1:end-3) - lon_vort_max).^2 + ...
            (data(i_time, 2:end-3) - lat_vort_max).^2) <= radius_threshold, 1);
        if isempty(i_wspd_max)
            continue;
        end
    end
    
    % 找到该风暴的中心位置
    lon_center = mean(data(i_time, 1:end-3));
    lat_center = mean(data(i_time, 2:end-3));
    
    % 初始化该风暴的轨迹
    if i_storm == 0 || size(storms_found{i_storm}.coords, 1) >= 2 &&...
            sqrt((storms_found{i_storm}.coords(end, 1) - lon_center)^2 + ...
            (storms_found{i_storm}.coords(end, 2) - lat_center)^2) > 5
        i_storm = i_storm + 1;
        storms_found{i_storm}.start_time = data(i_time, 3);
        storms_found{i_storm}.coords = [lon_center, lat_center];
    else
        storms_found{i_storm}.coords(end+1, :) = [lon_center, lat_center];
    end
    
    % 记录该风暴时间
    storms_found{i_storm}.times(end+1) = data(i_time, 3);
    
    % 记录该风暴的详细信息
    storms_found{i_storm}.irlatlon(end+1, :) = [lat_vort_max, lon_vort_max];
    storms_found{i_storm}.wspd850(end+1) = data(i_time, 5 + i_wspd_850_max);
    storms_found{i_storm}.wspd300(end+1) = data(i_time, 6 + i_wspd_300_max);
    storms_found{i_storm}.radii(end+1) = sqrt(sum((data(i_time, 1:end-3) - [lon_center, lat_center]).^2, 2))';
    storms_found{i_storm}.duration = data(i_time, 3) - storms_found{i_storm}.start_time;
end

% 打印所有风暴的详细信息
for i_storm = 1:length(storms_found)
    if storms_found{i_storm}.duration > min_duration
        disp('---------------------------------------------')
        disp(['Storm ', num2str(i_storm), ...
            ', Duration = ', num2str(storms_found{i_storm}.duration), ' h'])
        disp(storms_found{i_storm})
    end
end

% 绘制所有风暴的轨迹
figure
hold on
for i_storm = 1:length(storms_found)
    if storms_found{i_storm}.duration > min_duration
        plot(storms_found{i_storm}.coords(:, 1), storms_found{i_storm}.coords(:, 2), 'k-')
        plot(storms_found{i_storm}.coords(1, 1), storms_found{i_storm}.coords(1, 2), 'go')
        plot(storms_found{i_storm}.coords(end, 1), storms_found{i_storm}.coords(end, 2), 'rx')
    end
end
xlabel('Longitude')
ylabel('Latitude')

以下是一个MATLAB算法示例，用于满足所述条件并选择满足条件的时间步长，以及可视化结果：


```matlab
% 假设有一个包含时间和经纬度信息的数据矩阵 data，大小为 [time, lat, lon]
% 假设有一个包含不同高度层的数据矩阵 height_data，大小为 [time, height, lat, lon]
% 注意：请根据实际数据格式进行调整

% 参数设置
max_vorticity_threshold = 8.0e-5;  % 最大相对涡度阈值
max_wind_speed_threshold = 13.0;  % 最大风速阈值
temperature_thresholds = [0.8, 2.0, 0.5];  % 温度偏差阈值
radius_threshold = 200;  % 风暴半径阈值
min_duration = 36;  % 最小持续时间

% 初始化结果
satisfying_indices = [];

% 循环遍历每个时间步长
for t = 1:size(data, 1)
    % 获取当前时间步长的相关数据
    vorticity = data(t, :, :);
    wind_speed_850hPa = height_data(t, 1, :, :);
    wind_speed_300hPa = height_data(t, 2, :, :);
    temperature_300hPa = height_data(t, 3, :, :);
    temperature_500hPa = height_data(t, 4, :, :);
    temperature_700hPa = height_data(t, 5, :, :);
    
    % 检查条件
    if (max(vorticity(:)) > max_vorticity_threshold) ...
            && (max(wind_speed_850hPa(:)) > max_wind_speed_threshold) ...
            && (sum(temperature_300hPa(:)) - max(temperature_850hPa(:)) > temperature_thresholds(1)) ...
            && (sum(temperature_500hPa(:)) - max(temperature_850hPa(:)) > temperature_thresholds(2)) ...
            && (sum(temperature_700hPa(:)) - max(temperature_850hPa(:)) > temperature_thresholds(3)) ...
            && (max(wind_speed_850hPa(:)) > max(wind_speed_300hPa(:))) ...
            && (storm_radius < radius_threshold) ...
            && (storm_duration >= min_duration)
        
        % 符合条件，记录时间步长的索引
        satisfying_indices = [satisfying_indices, t];
    end
end

% 绘制符合条件的经纬度路径
if ~isempty(satisfying_indices)
    latitudes = [];  % 符合条件的纬度
    longitudes = [];  % 符合条件的经度
    
    % 提取符合条件的经纬度信息
    for t = satisfying_indices
        % 假设经纬度信息在数据矩阵的第二和第三维度
        latitudes = [latitudes, data(t, :, 2)];
        longitudes = [longitudes, data(t, :, 3)];
    end
    
    % 绘制路径
   plot(longitudes(:), latitudes(:), '-o');
  xlabel('经度');
  ylabel('纬度');
  title('满足条件的经纬度路径');

这段代码将会绘制符合条件的经纬度路径，通过连接这些点来显示路径。可以使用MATLAB的绘图函数（如plot）根据经度和纬度数据绘制路径，'-o'参数表示使用连线和圆圈标记绘制路径。您可以根据需要调整绘图样式和标签。

请注意，上述代码是基于假设的数据结构和阈值设置的示例代码。您需要根据您的实际数据和条件进行调整，确保数据加载、条件检查和路径绘制的正确性。

希望这个示例能对您有所帮助！

可以参考下

%%
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));