A method for calculating cloud occlusion probability using satellite observation products

Abstract

The present invention discloses a method for calculating cloud occlusion probability using satellite observation products, which relates to the field of remote sensing science and technology. The basic steps are: according to the latitude and longitude information and start and end time of two points in the study area, match the geostationary satellite data and read the cloud top height, cloud optical thickness, longitude and latitude information of all pixel points at the line of sight of the two points, and the cloud vertical distribution probability of long-term series data statistics; judge whether the cloud top height of each pixel point at the line of sight of the two points is greater than the flight altitude; calculate the average cloud optical thickness of the pixel points greater than the flight altitude, assign weights to obtain the weighted cloud optical thickness at the current flight altitude, and judge whether it is greater than the visible threshold; calculate the ratio of cloudy days to total days, and obtain the cloud occlusion probability between two points on the flight route. The present invention uses satellite observation products to obtain cloud horizontal and vertical distribution information, calculates the cloud occlusion probability between two points on the flight route, and has the advantages of objective and reasonable satellite observation data and high accuracy in calculating occlusion probability.

Description

Technical Field

[0001] The invention relates to the field of remote sensing science and technology, and in particular to a method for calculating cloud occlusion probability using satellite observation products. Background Art

[0002] The three-dimensional spatiotemporal distribution of clouds has a significant impact on the use of air-based and space-based optical and infrared sensors. For example, thick clouds may completely block the passage of light during optical imaging. Cloud cover has an important impact on the radiation balance of the ground-atmosphere system and the balance of the atmosphere, especially in low-latitude sea areas. The amount of cloud cover has an important impact on thermohaline circulation, heat exchange, and sea-air interaction. Although aviation has become the first ultra-safe system in the history of transportation with the increasing sophistication of airborne meteorological equipment, weather is still the main cause of flight safety and aviation accidents.

[0003] Ground observations have high detection accuracy for bottom-layer clouds, but observations of multi-layer clouds are easily affected by subjective judgment and weather conditions, and because the observation stations are highly localized and unevenly distributed around the world, it is impossible to detect large areas. Satellite data has many advantages, such as wide coverage, high repetition frequency, and complete cloud parameters. New-generation geostationary meteorological satellites such as Himawari-8 have high temporal resolution and can provide near-real-time, full-coverage, horizontally distributed cloud observation products for the entire layer of the atmosphere within the measurement area. CloudSat satellites can provide vertically distributed cloud observation products, laying a data foundation for obtaining three-dimensional information on cloud distribution based entirely on satellite data. In view of this, the present invention proposes a method for calculating cloud occlusion probability using satellite observation products by combining Himawari-8 satellites and CloudSat satellites, providing remote sensing information support for aircraft route design and flight safety. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] The present invention proposes a method for calculating cloud occlusion probability using geostationary satellite observation products. The method fully considers the horizontal and vertical distribution information of clouds and calculates the cloud occlusion probability on the flight route by combining Himawari-8 and CloudSat satellite observation products.

[0006] (II) Technical solution

[0007] The present invention comprises the following steps:

[0008] (1) Input the coordinates of the observation point (Px1, Py1), the coordinates of the target point on the same horizontal plane on the route (Px2, Py2), the flight altitude H, the start date t1 and the end date t2;

[0009] (2) acquiring Himawari-8 and CloudSat satellite data, wherein the Himawari-8 satellite data includes horizontally distributed cloud top height and cloud optical thickness cloud products, and the CloudSat satellite data includes vertical profile cloud products;

[0010] (3) Matching the Himawari-8 satellite fixed time data and CloudSat satellite seasonal statistical cloud vertical distribution probability data corresponding to the observation point and the target point;

[0011] (4) Read the cloud top height, cloud optical thickness, longitude and latitude information of the Himawari-8 satellite corresponding to all pixel points at the two-point line of sight, and the cloud vertical distribution probability data statistically obtained from the long-term series data of the CloudSat satellite;

[0012] (5) Determine whether the cloud top height of each pixel point on the two-point line of sight is greater than the flight height H. If it is greater than H, it is preliminarily determined that there are clouds; otherwise, it is determined that there are no clouds;

[0013] (6) Calculate the average cloud optical thickness Mean for the pixels whose cloud top height is greater than H at the two-point viewing distance CLOT According to the cloud vertical distribution probability information obtained by the CloudSat satellite, the weight ω of the average cloud optical thickness at height H is assigned to obtain the weighted cloud optical thickness σ CLOT ;

[0014] (7) Determination of weighted cloud optical thickness σ CLOT Is it greater than the visible threshold V? T , if greater than V T If the cloud is blocked, it is finally judged as cloud (occlusion), otherwise it is judged as cloudless;

[0015] (8) Calculate the ratio of the number of cloudy days observed by Himawari-8 between t1 and t2 to the total number of days (t2-t1) to obtain the probability of cloud cover between two points on the flight route over a period of time.

[0016] Furthermore, in step (2), the probability of cloud cover between two points on a flight route can be calculated by using only satellite data of horizontal and vertical cloud products.

[0017] Furthermore, in step (6), the optical thickness of the entire cloud layer of the satellite is weighted by using the CloudSat vertical statistical probability to obtain the corresponding optical thickness at the target altitude layer:

[0018] σ CLOT =Mean CLOT *ω (1)

[0019] In the formula, σ CLOT is the weighted cloud optical thickness, MeanCLOT is the average cloud optical thickness, ω is the probability of cloud vertical distribution based on the long-term series data of CloudSat satellite, and the probability value of having clouds at the height H.

[0020] (III) Beneficial effects

[0021] The advantages of the present invention are:

[0022] It is difficult to obtain the three-dimensional distribution information of clouds in the atmosphere, the observation information of a single satellite is incomplete, and the calculation uncertainty of cloud occlusion probability is high. The present invention combines the Himawari-8 satellite and the CloudSat satellite to integrate the horizontal and vertical distribution information of clouds, reduce the loss of data, and accurately obtain the cloud occlusion probability between the observation point and the target point on the flight route. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A flow chart of the steps for implementing the present invention. DETAILED DESCRIPTION

[0024] In order to make the purpose, content, and advantages of the present invention more clear, the specific implementation methods of the present invention are further described in detail below in conjunction with the accompanying drawings and embodiments:

[0025] Reference Figure 1 , the specific implementation steps of the present invention are:

[0026] (1) Input the coordinates of the observation point (Px1, Py1), the coordinates of the target point on the same horizontal plane on the route (Px2, Py2), the flight altitude H, the start date t1, and the end date t2.

[0027] (2) Acquire Himawari-8 and CloudSat satellite data, wherein the Himawari-8 satellite data includes horizontally distributed cloud top height and cloud optical thickness cloud products, and the CloudSat satellite data includes vertical profile cloud products.

[0028] (3) Match the Himawari-8 satellite fixed-time data and CloudSat satellite seasonal statistical cloud vertical distribution probability data corresponding to the observation point and the target point.

[0029] (4) Read the cloud top height, cloud optical thickness, longitude and latitude information of the Himawari-8 satellite corresponding to all pixel points at the two-point line of sight, and the cloud vertical distribution probability data statistically obtained from the long-term series data of the CloudSat satellite.

[0030] (5) Determine whether the cloud top height of each pixel point on the two-point line of sight is greater than the flight height H. If it is greater than H, it is preliminarily determined that there are clouds; otherwise, it is determined that there are no clouds.

[0031] (6) Calculate the average cloud optical thickness Mean for the pixels whose cloud top height is greater than H at the two-point viewing distance CLOT According to the cloud vertical distribution probability information obtained by the CloudSat satellite, the weight ω of the average cloud optical thickness at height H is assigned to obtain the weighted cloud optical thickness σ CLOT .

[0032] (7) Determination of weighted cloud optical thickness σ CLOT Is it greater than the visible threshold V? T , if greater than V T If the cloud is blocked, it is finally judged as cloud (occlusion), otherwise it is judged as cloudless;

[0033] Among them, the visible threshold V T It can be calculated from the required visibility value (in km) using open source radiative transfer models such as MODTRAN or 6S.

[0034] (8) Calculate the ratio of the number of cloudy days observed by Himawari-8 between t1 and t2 to the total number of days (t2-t1) to obtain the probability of cloud cover between two points on a certain flight route over a period of time.

[0035] Furthermore, in step (2), the probability of cloud cover between two points on a flight route can be calculated by using only satellite data of horizontal and vertical cloud products.

[0036] Furthermore, in step (6), the optical thickness of the entire cloud layer of the satellite is weighted by using the CloudSat vertical statistical probability to obtain the corresponding optical thickness at the target altitude layer:

[0037] σ CLOT =Mean CLOT *ω (1)

[0038] In the formula, σ CLOT is the weighted cloud optical thickness, Mean CLOT is the average cloud optical thickness, ω is the probability of cloud vertical distribution based on the long-term series data of CloudSat satellite, and the probability value of having clouds at the height H.

Claims

1. A method for calculating cloud occlusion probability using satellite observation products, characterized in that: The following steps are involved: (1) Input the coordinates of the observation point (Px1, Py1), the coordinates of the target point on the same horizontal plane on the route (Px2, Py2), the flight altitude H, the start date t1 and the end date t2; (2) acquiring Himawari-8 and CloudSat satellite data, wherein the Himawari-8 satellite data includes horizontally distributed cloud top height and cloud optical thickness cloud products, and the CloudSat satellite data includes vertical profile cloud products; (3) Matching the Himawari-8 satellite fixed time data and CloudSat satellite seasonal statistical cloud vertical distribution probability data corresponding to the observation point and the target point; (4) Read the cloud top height, cloud optical thickness, longitude and latitude information of the Himawari-8 satellite corresponding to all pixel points at the two-point line of sight, and the cloud vertical distribution probability data statistically obtained from the long-term series data of the CloudSat satellite; (5) Determine whether the cloud top height of each pixel point on the two-point line of sight is greater than the flight height H. If it is greater than H, it is preliminarily determined that there are clouds; otherwise, it is determined that there are no clouds; (6) Calculate the average cloud optical thickness Mean for the pixels whose cloud top height is greater than H at the two-point viewing distance CLOT According to the cloud vertical distribution probability information obtained by the CloudSat satellite, the weight ω of the average cloud optical thickness at height H is assigned to obtain the weighted cloud optical thickness σ CLOT ; (7) Determination of weighted cloud optical thickness σ CLOT Is it greater than the visible threshold V T , if greater than V T The final judgment is that there are clouds, otherwise it is judged as no clouds; (8) Calculate the ratio of the number of cloudy days observed by Himawari-8 between t1 and t2 to the total number of days, and obtain the probability of cloud cover between two points on a certain flight route within a period of time; In the above step (2), the probability of cloud cover between two points on a flight route can be calculated by using only the horizontal and vertical cloud product data observed by satellite.

2. A method for calculating cloud cover probability using satellite observation products according to claim 1, characterized in that: In the step (6), the optical thickness of the entire cloud layer of the geostationary satellite is weighted by using the CloudSat vertical statistical probability to obtain the corresponding optical thickness at the target altitude layer: s CLOT =Mean CLOT *oh (1) In the formula, σ CLOT is the weighted cloud optical thickness, Mean CLOT is the average cloud optical thickness, ω is the probability of cloud vertical distribution based on the long-term series data of CloudSat satellite, and the probability value of having clouds at the height H.

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