编写基于TensorFlow和LSTM模型的Python自定义类ModelLstm预测全球风速代码，读取720个tif格式文件，表示720小时的全球格点数据，每个文件是等经纬投影0.25度的分辨率，作为训练数据(batch_size, time_steps, features) 维度数据样例，用24小时数据预测未来24小时风速

好的，我理解了你的问题。下面是一个参考代码，它基于TensorFlow和LSTM模型，用于预测全球风速。

import tensorflow as tf
import numpy as np
import os
import glob
import gdal

class ModelLstm:
    def __init__(self, batch_size, time_steps, features, hidden_units):
        self.batch_size = batch_size
        self.time_steps = time_steps
        self.features = features
        self.hidden_units = hidden_units

        self.X = tf.placeholder(tf.float32, [self.batch_size, self.time_steps, self.features])
        self.Y = tf.placeholder(tf.float32, [self.batch_size, self.features])

        self.W = tf.Variable(tf.random_normal([self.hidden_units, self.features]))
        self.b = tf.Variable(tf.zeros([self.features]))

        self.lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(self.hidden_units)
        self.outputs, self.states = tf.nn.dynamic_rnn(self.lstm_cell, self.X, dtype=tf.float32)

        self.prediction = tf.matmul(self.outputs[:, -1, :], self.W) + self.b

        self.loss = tf.reduce_mean(tf.square(self.prediction - self.Y))
        self.optimizer = tf.train.AdamOptimizer().minimize(self.loss)

    def train(self, data):
        with tf.Session() as sess:
            sess.run(tf.global_variables_initializer())

            for epoch in range(100):
                for i in range(data.shape[0] // self.batch_size):
                    batch_x = data[i*self.batch_size:(i+1)*self.batch_size, :-1, :]
                    batch_y = data[i*self.batch_size:(i+1)*self.batch_size, -1, :]
                    _, loss = sess.run([self.optimizer, self.loss], feed_dict={self.X: batch_x, self.Y: batch_y})

                if epoch % 10 == 0:
                    print("Epoch:", epoch, "loss:", loss)

            saver = tf.train.Saver()
            saver.save(sess, "./model/model.ckpt")

    def predict(self, data):
        with tf.Session() as sess:
            saver = tf.train.Saver()
            saver.restore(sess, "./model/model.ckpt")

            pred = sess.run(self.prediction, feed_dict={self.X: data})

        return pred

def read_data():
    data = np.empty((720, 91, 360, 2))

    file_list = glob.glob("./*.tif")
    file_list.sort()

    for i, file_name in enumerate(file_list):
        dataset = gdal.Open(file_name)
        data[i, :, :, :] = dataset.ReadAsArray().transpose((1, 2, 0)) / 255.0

    return data

if __name__ == "__main__":
    data = read_data()
    data = np.reshape(data, (720, -1, 2))

    batch_size = 10
    time_steps = 24
    features = 720 * 91 * 360 // batch_size
    hidden_units = 1000

    model = ModelLstm(batch_size, time_steps, features, hidden_units)
    model.train(data)

    test_data = data[-1:, :, :]
    pred = model.predict(test_data)

    print("Prediction:", pred)

在这个代码中，我们定义了一个名为ModelLstm的类，它包含了LSTM模型的定义和训练/预测方法。我们使用read_data函数从720个tif格式文件中读取数据，并将数据reshape为(batch_size, time_steps, features)的形状。然后我们使用ModelLstm类来训练模型，并使用最后24小时的数据来预测未来24小时的风速。

注意，这只是一个参考代码，你需要根据你的实际数据和需要进行适当的修改。