Method for estimating cloudy day atmosphere precipitable water amount based on meteorological satellite

Abstract

The invention provides a cloud sky atmosphere precipitable water amount estimation method based on a meteorological satellite, and belongs to the technical field of meteorological satellite detection. And carrying out cloud detection by using a relative humidity threshold method based on an atmospheric sounding profile, and carrying out radiation simulation calculation on near-infrared and thermal infrared water vapor channels of a meteorological satellite under a cloudy day condition by using an atmospheric radiation transfer mode MODTRAN. The near-infrared and thermal-infrared water vapor channels can detect the rainfall of the atmosphere on the cloud, and the numerical forecasting mode can provide the temperature and humidity profile distribution of the whole atmosphere and the total rainfall of the atmosphere. It is assumed that the atmospheric profile form of numerical forecasting is correct, but deviation correction needs to be carried out. The radiation of the near-infrared and thermal infrared water vapor channels and the temperature and humidity profiles of the mode forecast are used as input, and a cloud sky atmosphere precipitable water estimation model is constructed by using a convolutional neural network. According to the method, multi-band observation and numerical forecasting mode information are used, the atmospheric radiation transmission process is considered, and the precision of the obtained cloudy atmosphere precipitable water is high.

Description

Technical Field

[0001] The invention belongs to the technical field of meteorological satellite detection, and in particular relates to a method for estimating cloud-sky atmospheric precipitable water based on meteorological satellites. Background Art

[0002] Water vapor is a key factor in the Earth's climate system, which has a significant impact on weather processes, climate change, atmospheric chemistry and dynamics. Atmospheric precipitable water is the total atmospheric water vapor content contained in the vertical column of a cross-sectional unit, and is an important indicator for measuring the amount of atmospheric water vapor. Atmospheric precipitable water has important applications in weather forecasting, artificial weather modification, disastrous weather and climate change. In addition, atmospheric precipitable water is also a necessary parameter for atmospheric correction in surface satellite remote sensing. For example, accurate estimates of surface temperature, surface reflectivity and surface solar radiation based on satellite observations require relatively accurate atmospheric precipitable water data.

[0003] Accurate atmospheric precipitable water can be obtained by using atmospheric sounding balloons to obtain atmospheric temperature and humidity profiles, and then integrating them. However, sounding balloons are expensive and can only obtain atmospheric precipitable water at specific locations. Sounding sites are unevenly distributed and the number is small. Some ground-based remote sensing equipment such as sun photometers, microwave radiometers, and ground-based GPS can also obtain atmospheric precipitable water, but these ground-based remote sensing equipment can only obtain point observations and cannot obtain a large range of atmospheric precipitable water spatial distribution.

[0004] In contrast, meteorological satellite remote sensing can obtain a wide range of atmospheric precipitable water information. At present, meteorological satellite remote sensing detection of atmospheric water vapor is mainly carried out using near-infrared, infrared and microwave bands. The water vapor detection accuracy of near-infrared and infrared bands is higher than that of microwaves, but due to their short wavelengths, they cannot penetrate clouds, so near-infrared and infrared mainly obtain atmospheric precipitable water under clear sky conditions. Microwaves have a longer wavelength and can penetrate clouds, so they can obtain atmospheric precipitable water in the cloud sky, but their spatial resolution is poor. In addition, due to the large dynamic range of surface microwave emissivity, the signal-to-noise ratio of satellite-borne microwave radiometers is poor compared with visible light and infrared channels, which makes microwave detection of atmospheric precipitable water less accurate. More than 60% of the earth's surface is covered by clouds, and the annual average cloud cover in some areas is more than 70%. Therefore, high-spatial-resolution cloud-sky atmospheric precipitable water detection based on meteorological satellites is an urgent problem to be solved, which requires the estimation of cloud-sky atmospheric precipitable water based on near-infrared and infrared channel data, but there is no relevant research at present. Summary of the invention

[0005] The purpose of the present invention is to solve the defects of the above-mentioned prior art and provide a method for estimating the amount of precipitable water in the atmosphere of clouds and sky based on meteorological satellites, which solves the shortcomings of the existing methods for estimating the amount of precipitable water in the atmosphere of clouds and sky based on meteorological satellites.

[0006] The present invention adopts the following technical solution:

[0007] A method for estimating cloud-sky atmospheric precipitable water based on meteorological satellites, comprising:

[0008] Step 1. Collect atmospheric sounding balloon atmospheric profile data and numerical forecast model atmospheric profile data, screen outliers for atmospheric sounding balloon atmospheric profile data and numerical forecast model atmospheric profile data, match these two types of atmospheric profiles in time and space, and form the sounding balloon atmospheric temperature, atmospheric humidity, pressure profile and the numerical forecast model atmospheric temperature, atmospheric humidity, pressure profile that match in time and space;

[0009] Step 2. Use the relative humidity threshold method to perform relative threshold cloud detection on the atmospheric temperature, atmospheric humidity, and pressure profiles collected by the atmospheric sounding balloon in step 1 to obtain the vertical distribution structure of atmospheric clouds, and further form a data set of atmospheric temperature, atmospheric humidity, and pressure profiles under cloud conditions that matches the time and space of the numerical forecast model;

[0010] Step 3. Using the medium spectral resolution atmospheric radiation transfer model MODTRAN and the atmospheric temperature, atmospheric humidity, and pressure profile data set under cloudy conditions collected in step 2, set the instrument characteristic parameters, solar altitude angle, and satellite observation angle of the meteorological satellite MODIS, and simulate the reflectivity and brightness temperature of the near-infrared and thermal infrared water vapor channels of the meteorological satellite MODIS under cloudy conditions;

[0011] Step 4. Perform layered vertical integration calculation on the atmospheric temperature, atmospheric humidity, and pressure profiles under the cloudy sky condition collected in step 2, and calculate the total atmospheric precipitable water in the upper cloud layer and the entire atmosphere of the atmospheric sounding balloon;

[0012] The layered vertical integration is: integrating the layered atmospheric temperature, atmospheric humidity, and pressure profiles to calculate the atmospheric precipitable water;

[0013] Integrate the atmospheric profile above the cloud layer to obtain the atmospheric precipitable water above the cloud layer;

[0014] Integrate the whole-layer atmospheric temperature, atmospheric humidity, and pressure profile to obtain the total atmospheric precipitable water in the whole layer of atmosphere;

[0015] The specific calculation is done by the following formula:

[0016]

[0017] Where PW is the atmospheric precipitable water, g is the gravitational acceleration, P s is the surface pressure or cloud top pressure, P t is the atmospheric pressure at the top of the profile, q υ It is the atmospheric humidity information.

[0018] Step 5. Take the reflectivity and brightness temperature of the near-infrared and thermal infrared water vapor channels of the meteorological satellite MODIS under the cloudy sky conditions simulated in step 3, the atmospheric temperature, atmospheric humidity, and pressure profile of the numerical forecast model as input, and the atmospheric temperature, atmospheric humidity, and atmospheric precipitable water of the atmospheric sounding balloon in step 4 as output, perform model training, model verification, and error evaluation, and use a convolutional neural network to construct a cloud-sky atmospheric precipitable water estimation model;

[0019] Step 6. Use MODIS cloud detection products to distinguish between cloudy and clear sky pixels, and then extract the reflectivity and brightness temperature of the near-infrared and thermal infrared water vapor channels actually observed by MODIS under cloudy conditions. According to the longitude and latitude of the observation pixels of the meteorological satellite MODIS, extract the atmospheric temperature, atmospheric humidity, and pressure profiles of the numerical forecast model, and input these data into the cloudy atmospheric precipitable water estimation model constructed in step 5 to estimate the cloudy atmospheric precipitable water.

[0020] Furthermore, the outlier screening is to determine whether the atmospheric temperature, atmospheric humidity, and pressure profile exceed the maximum and minimum values ​​of the historical climate data. If they exceed 20% of the maximum and minimum values ​​of the historical climate data in recent decades, they are determined to be outliers.

[0021] Furthermore, the time and space matching is based on the position and observation time of the atmospheric sounding balloon, and selects the observation grid profile of the numerical prediction model closest to the position of the atmospheric sounding balloon as the data for spatial matching, and selects the forecast time profile of the numerical prediction model closest to the observation time of the atmospheric sounding balloon as the data for time matching.

[0022] Furthermore, the relative humidity threshold method is: when the relative humidity is greater than 91% in the altitude range of 0-1.0km, it is judged as cloud; when the altitude is greater than 1.0km and less than 2.0km, the relative humidity is greater than 87%, it is judged as cloud; when the altitude is greater than 2.0km and less than 7.0km, the relative humidity is greater than 82%, it is judged as cloud; when the altitude is greater than 7.0km and less than 10.0km, the relative humidity is greater than 72%, it is judged as cloud; when the altitude is greater than 10.0km, the relative humidity is greater than 67%, it is judged as cloud.

[0023] Furthermore, the instrument characteristic parameters of the meteorological satellite MODIS include: central wavelength, instrument response function, instantaneous field of view, spatial resolution and instrument noise.

[0024] Furthermore, the meteorological satellite MODIS near-infrared and thermal infrared water vapor channels are: MODIS's 0.89-0.96μm near-infrared water vapor absorption channel, MODIS's 6.5-8.7μm infrared water vapor absorption channel and MODIS's 10.7-12.3μm thermal infrared water vapor channel.

[0025] Furthermore, the MODIS cloud detection product is the cloud cover mask data of each pixel of the meteorological satellite MODIS, based on which it can be determined whether the observed pixel is a cloudy sky.

[0026] Beneficial effects of the present invention:

[0027] The present invention uses a relative humidity threshold to perform relative threshold cloud detection processing on the atmospheric sounding profile, obtains the atmospheric temperature, atmospheric humidity, and pressure profile under the condition of cloudy sky, and uses the atmospheric radiation transmission model MODTRAN to perform radiation simulation calculation of the near infrared and thermal infrared water vapor channels of the meteorological satellite under the condition of cloudy sky. The atmospheric profile is layered and vertically integrated to obtain the atmospheric precipitable water above the cloud top and the entire layer, and the relationship between the atmospheric precipitable water above the cloud top and the atmospheric precipitable water of the entire layer is analyzed. The near infrared and thermal infrared water vapor channels can detect the atmospheric precipitable water on the cloud, and the numerical prediction model can provide the distribution of the atmospheric temperature, atmospheric humidity, and pressure profile of the entire layer. Assuming that the form of the numerically predicted atmospheric profile is correct, but deviation correction is required, the radiation of the near infrared and thermal infrared water vapor channels, the atmospheric temperature, atmospheric humidity, and pressure profile predicted by the model are used as inputs, and a cloud sky atmospheric precipitable water estimation model is constructed using a convolutional neural network. The present invention uses multi-band observation and numerical prediction model information, and the atmospheric radiation transmission process is considered during modeling, so that the obtained cloud sky atmospheric precipitable water has a high accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 is a flow chart of the steps of the present invention;

[0029] Figure 2 The present invention is a schematic diagram of the process of calculating the reflectivity and brightness temperature of near-infrared and thermal-infrared water vapor channels using the radiation transfer model. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention is described clearly and completely below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] The present invention specifically relates to a method for estimating atmospheric precipitable water in a cloud-like sky based on a meteorological satellite, including collecting atmospheric sounding balloons and atmospheric profile data of the Global Forecast System (GFS) model of the National Center for Environmental Prediction of the United States, performing relative threshold cloud detection based on the atmospheric sounding profile using a relative humidity threshold method, and using an atmospheric radiation transmission model MODTRAN to perform radiation simulation calculations of near-infrared and thermal-infrared water vapor channels of a meteorological satellite Moderate Resolution Imaging Spectrometer (MODIS) under cloud-like conditions. The near-infrared and thermal-infrared water vapor channels of the meteorological satellite MODIS can detect atmospheric precipitable water on clouds, and the GFS numerical prediction model can provide the temperature, atmospheric humidity, pressure profile distribution and total atmospheric precipitable water of the entire layer of atmosphere. Assuming that the atmospheric profile form of the GFS is correct, the radiation of the near-infrared and thermal-infrared water vapor channels of the meteorological satellite MODIS, the atmospheric temperature, atmospheric humidity and pressure profile predicted by the GFS model are used as inputs, and a cloud-like atmospheric precipitable water estimation model is constructed using a convolutional neural network.

[0032] like Figure 1-Figure 2 As shown, a method for estimating the amount of precipitable water in the sky based on meteorological satellites of the present invention specifically includes the following contents:

[0033] Step 1. Collect atmospheric sounding balloon atmospheric profile data and GFS numerical forecast model atmospheric profile data, screen outliers for atmospheric sounding balloon atmospheric profile data and GFS numerical forecast model atmospheric profile data, match these two types of atmospheric profiles in time and space, and form the sounding balloon atmospheric temperature, atmospheric humidity, pressure profile and GFS numerical forecast model atmospheric temperature, atmospheric humidity, pressure profile that match in time and space;

[0034] Among them, the outlier screening is to determine whether the atmospheric temperature, atmospheric humidity, and pressure profiles exceed the maximum and minimum values ​​of the historical climate data. If they exceed 20% of the maximum and minimum values ​​of the historical climate data in the past ten years, they are judged as outliers. Time and space matching is based on the position and observation time of the atmospheric sounding balloon, and the GFS numerical prediction model observation grid profile closest to the position of the atmospheric sounding balloon is selected as the spatial matching data, and the GFS numerical prediction model forecast time profile closest to the atmospheric sounding balloon observation time is selected as the time matching data.

[0035] Step 2. Use the relative humidity threshold method to perform relative threshold cloud detection on the atmospheric temperature, atmospheric humidity, and pressure profiles collected in step 1 to obtain the vertical distribution structure of atmospheric clouds, and further form a dataset of atmospheric temperature, atmospheric humidity, and pressure profiles under cloud conditions that matches the GFS numerical forecast model in time and space;

[0036] Among them, the relative humidity threshold method is: when the relative humidity is greater than 91% in the altitude range of 0-1.0km, it is judged as cloud; when the altitude is greater than 1.0km and less than 2.0km, the relative humidity is greater than 87%, it is judged as cloud; when the altitude is greater than 2.0km and less than 7.0km, the relative humidity is greater than 82%, it is judged as cloud; when the altitude is greater than 7.0km and less than 10.0km, the relative humidity is greater than 72%, it is judged as cloud; when the altitude is greater than 10.0km, the relative humidity is greater than 67%, it is judged as cloud.

[0037] Step 3. Using the medium spectral resolution atmospheric radiation transfer model MODTRAN and the atmospheric temperature, atmospheric humidity and pressure profile data set under cloudy conditions collected in step 2, set the meteorological satellite MODIS instrument characteristic parameters, solar altitude angle and satellite observation angle, and simulate the reflectivity and brightness temperature of the near-infrared and thermal infrared water vapor channels of the meteorological satellite MODIS under cloudy conditions;

[0038] Among them, the characteristic parameters of the meteorological satellite MODIS instrument include: central wavelength, instrument response function, instantaneous field of view, spatial resolution and instrument noise. The near-infrared and thermal infrared water vapor channels of the meteorological satellite MODIS are: MODIS's 0.89-0.96μm near-infrared water vapor absorption channel, MODIS's 6.5-8.7μm infrared water vapor absorption channel and MODIS's 10.7-12.3μm thermal infrared water vapor channel.

[0039] Step 4. Perform layered vertical integration calculations on the atmospheric temperature, atmospheric humidity, and pressure profiles collected in step 2 under cloudy conditions to obtain the total atmospheric precipitable water in the upper cloud layer and the entire atmosphere from the atmospheric sounding balloon and the GFS numerical forecast model.

[0040] Among them, the stratified vertical integration is to integrate the stratified atmospheric temperature, atmospheric humidity, and pressure profile to calculate the atmospheric precipitable water.

[0041] The total atmospheric precipitable water is obtained by integrating the temperature, atmospheric humidity and pressure profile of the entire atmosphere, and the atmospheric profile above the cloud layer is integrated to obtain the atmospheric precipitable water above the cloud layer.

[0042] The specific calculation is done by the following formula:

[0043]

[0044] Where PW is the atmospheric precipitable water, g is the gravitational acceleration, P s is the surface pressure or cloud top pressure, P t is the atmospheric pressure at the top of the profile, q υ It is the atmospheric humidity information.

[0045] Step 5. Take the reflectivity and brightness temperature of the near-infrared and thermal infrared water vapor channels of the meteorological satellite MODIS under the cloudy sky conditions simulated in step 3, the atmospheric temperature, atmospheric humidity, and pressure profile of the GFS numerical forecast model as input, and the atmospheric temperature, atmospheric humidity, and atmospheric precipitable water of the layered vertical integration of the atmospheric temperature, atmospheric humidity, and pressure profile of the atmospheric sounding balloon in step 4 as output, perform model training, model verification, and error evaluation, and use a convolutional neural network to construct a cloudy sky atmospheric precipitable water estimation model.

[0046] Step 6. Use MODIS cloud detection products to distinguish between cloudy and clear sky pixels, and then extract the reflectivity and brightness temperature of the near-infrared and thermal infrared water vapor channels actually observed by MODIS under cloudy conditions. According to the longitude and latitude of the observation pixels of the meteorological satellite MODIS, extract the atmospheric temperature, atmospheric humidity, and pressure profiles of the GFS numerical forecast model. Input these data into the cloudy atmospheric precipitable water estimation model constructed in step 5 to estimate the cloudy atmospheric precipitable water.

[0047] Among them, the MODIS cloud detection product is the cloud coverage mask data of each pixel of the meteorological satellite MODIS. Based on this data, it can be determined whether the observed pixel is a cloudy sky.

[0048] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for estimating the amount of precipitable water in the sky based on meteorological satellites, characterized in that: include: Step 1. Collect atmospheric sounding balloon atmospheric profile data and numerical forecast model atmospheric profile data, screen outliers for atmospheric sounding balloon atmospheric profile data and numerical forecast model atmospheric profile data, match these two types of atmospheric profiles in time and space, and form the sounding balloon atmospheric temperature, atmospheric humidity, pressure profile and the numerical forecast model atmospheric temperature, atmospheric humidity, pressure profile that match in time and space; Step 2. Use the relative humidity threshold method to perform relative threshold cloud detection on the atmospheric temperature, atmospheric humidity, and pressure profiles collected by the atmospheric sounding balloon in step 1 to obtain the vertical distribution structure of atmospheric clouds, and further form a data set of atmospheric temperature, atmospheric humidity, and pressure profiles under cloud conditions that matches the time and space of the numerical forecast model; Step 3. Using the medium spectral resolution atmospheric radiation transfer model MODTRAN and the atmospheric temperature, atmospheric humidity, and pressure profile data set under cloudy conditions collected in step 2, set the instrument characteristic parameters, solar altitude angle, and satellite observation angle of the meteorological satellite MODIS, and simulate the reflectivity and brightness temperature of the near-infrared and thermal infrared water vapor channels of the meteorological satellite MODIS under cloudy conditions; Step 4. Perform layered vertical integration calculation on the atmospheric temperature, atmospheric humidity, and pressure profiles under the cloudy sky condition collected in step 2, and calculate the total atmospheric precipitable water in the upper cloud layer and the entire atmosphere of the atmospheric sounding balloon; The layered vertical integration is: integrating the layered atmospheric temperature, atmospheric humidity, and pressure profiles to calculate the atmospheric precipitable water; Integrate the whole-layer atmospheric temperature, atmospheric humidity, and pressure profile to obtain the total atmospheric precipitable water for the whole layer of atmosphere, and integrate the atmospheric profile above the cloud layer to obtain the atmospheric precipitable water above the cloud layer; The specific calculation is done by the following formula: Where PW is the atmospheric precipitable water, g is the gravitational acceleration, P s is the surface pressure or cloud top pressure, P t is the atmospheric pressure at the top of the profile, q υ It is the atmospheric humidity information; Step 5. Take the reflectivity and brightness temperature of the near-infrared and thermal infrared water vapor channels of the meteorological satellite MODIS under the cloudy sky conditions simulated in step 3, the atmospheric temperature, atmospheric humidity, and pressure profile of the numerical forecast model as input, and the atmospheric temperature, atmospheric humidity, and atmospheric precipitable water of the atmospheric sounding balloon in step 4 as output, perform model training, model verification, and error evaluation, and use a convolutional neural network to construct a cloud-sky atmospheric precipitable water estimation model; Step 6. Use MODIS cloud detection products to distinguish between cloudy and clear sky pixels, and then extract the reflectivity and brightness temperature of the near-infrared and thermal infrared water vapor channels actually observed by MODIS under cloudy conditions. According to the longitude and latitude of the pixels observed by the meteorological satellite MODIS, extract the atmospheric temperature, atmospheric humidity, and pressure profiles of the numerical forecast model. Input these data into the cloudy atmospheric precipitable water estimation model constructed in step 5 to estimate the cloudy atmospheric precipitable water.

2. The method for estimating the amount of precipitable water in the sky based on meteorological satellite according to claim 1, characterized in that: The outlier screening described in step 1 is to determine whether the atmospheric temperature, atmospheric humidity, and pressure profile exceed the maximum and minimum values ​​of the historical climate data. If they exceed 20% of the maximum and minimum values ​​of the historical climate data in recent decades, they are determined to be outliers; The time and space matching is based on the position and observation time of the atmospheric sounding balloon, and selects the numerical prediction model observation grid profile closest to the position of the atmospheric sounding balloon as the spatial matching data, and selects the forecast time profile of the numerical prediction model closest to the observation time of the atmospheric sounding balloon as the time matching data.

3. The method for estimating the amount of precipitable water in the sky based on meteorological satellite according to claim 1, characterized in that: In step 2, the relative humidity threshold method is: when the relative humidity is greater than 91% in the altitude range of 0-1.0km, it is judged as cloud; when the altitude is greater than 1.0km and less than 2.0km, the relative humidity is greater than 87%, it is judged as cloud; when the altitude is greater than 2.0km and less than 7.0km, the relative humidity is greater than 82%, it is judged as cloud; when the altitude is greater than 7.0km and less than 10.0km, the relative humidity is greater than 72%, it is judged as cloud; when the altitude is greater than 10.0km, the relative humidity is greater than 67%, it is judged as cloud.

4. The method for estimating the amount of precipitable water in the sky based on meteorological satellite according to claim 1, characterized in that: In step 3, the instrument characteristic parameters of the meteorological satellite MODIS include: central wavelength, instrument response function, instantaneous field of view, spatial resolution and instrument noise; The near-infrared and thermal-infrared water vapor channels of the meteorological satellite MODIS are: 0.89-0.96μm near-infrared water vapor absorption channel, 6.5-8.7μm infrared water vapor absorption channel and 10.7-12.3μm thermal infrared water vapor channel.

5. The method for estimating the amount of precipitable water in the sky based on meteorological satellite according to claim 1, characterized in that: In step 6, the MODIS cloud detection product is the cloud cover mask data of each pixel of the meteorological satellite MODIS, and based on this data, it is determined whether the observed pixel is a cloudy sky.

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