A method for estimating precipitable water in cloud and sky atmosphere based on meteorological satellites

Abstract

The present invention provides a method for estimating the precipitable water in cloud and sky atmosphere based on meteorological satellites, belonging to the technical field of meteorological satellite detection. Cloud detection is carried out using the relative humidity threshold method based on the atmospheric sounding profile, and the radiative simulation calculations of the near-infrared and thermal-infrared water vapor channels of meteorological satellites in the case of cloud and sky are carried out using the atmospheric radiative transfer model MODTRAN. The near-infrared and thermal-infrared water vapor channels can detect the precipitable water in the atmosphere above the cloud, and the numerical prediction model can provide the temperature, humidity profile distribution of the whole atmosphere and the total precipitable water in the atmosphere. It is assumed that the form of the atmospheric profile in the numerical prediction is correct, but bias correction is required. Taking the radiation of the near-infrared and thermal-infrared water vapor channels and the temperature and humidity profiles predicted by the model as inputs, a convolutional neural network is used to construct an estimation model for the precipitable water in cloud and sky atmosphere. The present invention uses multi-band observations and numerical prediction model information, takes into account the atmospheric radiative transfer process, and obtains a relatively high accuracy of the precipitable water in cloud and sky atmosphere.

Description

Technical Field

[0001] The present invention belongs to the technical field of meteorological satellite detection, and particularly relates to a method for estimating precipitable water in cloud and sky atmosphere based on meteorological satellites. Background Technique

[0002] Water vapor is a key factor in the Earth's climate system, which has an important impact on weather processes, climate change, atmospheric chemistry and dynamics. Precipitable water in the atmosphere is the total atmospheric water vapor content contained in the vertical column of the cross-sectional unit, and is an important indicator to measure the amount of atmospheric water vapor. Precipitable water in the atmosphere has important applications in weather forecasting, artificial weather modification, severe weather and climate change fields. In addition, precipitable water in the atmosphere is also a necessary parameter required for atmospheric correction during satellite remote sensing of the surface. For example, accurate estimation of surface temperature, surface reflectance and surface solar radiation based on satellite observations requires relatively accurate precipitable water data.

[0003] Accurate precipitable water in the atmosphere can be obtained by acquiring the atmospheric temperature and humidity profiles through radiosondes and then integrating them. However, radiosondes are costly and can only obtain the precipitable water in the atmosphere at specific locations. The distribution of radiosonde stations is uneven and the number is small. Some ground-based remote sensing devices such as sun photometers, microwave radiometers, and ground-based GPS can also obtain precipitable water in the atmosphere, but these ground-based remote sensing devices can only obtain point observations and cannot obtain the spatial distribution of precipitable water over a large area.

[0004] In contrast, meteorological satellite remote sensing can obtain information on precipitable water over a large area. Currently, meteorological satellite remote sensing for detecting atmospheric water vapor mainly uses the near-infrared, infrared and microwave bands. The accuracy of water vapor detection in the near-infrared and infrared bands is higher than that in the microwave band. However, due to their short wavelengths, they cannot penetrate clouds. Therefore, the near-infrared and infrared mainly obtain the precipitable water in the atmosphere under clear sky conditions. The microwave wavelength is longer and can penetrate clouds, and can obtain the precipitable water in cloud and sky atmosphere, but its spatial resolution is poor. In addition, due to the large dynamic range of the surface microwave emissivity, the signal-to-noise ratio of spaceborne microwave radiometers is worse than that of visible and infrared channels, which makes the accuracy of microwave detection of precipitable water in the atmosphere poor. More than 60% of the Earth's surface area is covered by clouds, and the annual average cloud cover in some areas reaches more than 70%. Therefore, the detection of precipitable water in cloud and sky atmosphere with high spatial resolution based on meteorological satellites is an urgent problem to be solved, which requires the development of an estimation method for precipitable water in cloud and sky atmosphere based on near-infrared and infrared channel data, but there is no relevant research yet. Summary of the Invention

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

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

[0007] A method for estimating the precipitable water vapor in the cloud and sky atmosphere based on meteorological satellites, comprising:

[0008] Step 1. Collect the atmospheric sounding balloon atmospheric profile data and the numerical weather prediction model atmospheric profile data, screen the outliers of the atmospheric sounding balloon atmospheric profile data and the numerical weather prediction model atmospheric profile data, perform the time and space matching of these two types of atmospheric profiles, and form the space-time matched atmospheric temperature, atmospheric humidity, pressure profiles of the sounding balloon and the atmospheric temperature, atmospheric humidity, pressure profiles of the numerical weather prediction model;

[0009] Step 2. Use the relative humidity threshold method to detect the relative threshold cloud of the atmospheric temperature, atmospheric humidity, and pressure profiles of the atmospheric sounding balloon collected in Step 1, obtain the vertical cloud distribution structure of the atmosphere, and further form the dataset of the atmospheric temperature, atmospheric humidity, and pressure profiles under the cloud and sky conditions that are matched with the numerical weather prediction model in terms of time and space;

[0010] Step 3. Use the medium spectral resolution atmospheric radiation transfer model MODTRAN and the dataset of the atmospheric temperature, atmospheric humidity, and pressure profiles under the cloud and sky conditions collected in Step 2, set the instrument characteristic parameters of the meteorological satellite MODIS, the solar altitude angle, and the satellite observation angle, and perform the simulation calculation of the reflectivity and brightness temperature of the near-infrared and thermal-infrared water vapor channels of the meteorological satellite MODIS under the cloud and sky conditions;

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

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

[0013] Perform the integration of the atmospheric profile above the cloud to obtain the precipitable water vapor above the cloud;

[0014] Perform the integration of the entire atmospheric temperature, atmospheric humidity, and pressure profiles to obtain the total precipitable water vapor in the entire atmosphere;

[0015] Specifically, the calculation is carried out through the following formula:

[0016]

[0017] In the formula, PW is the precipitable water vapor, g is the acceleration of gravity, P s is the ground pressure or cloud top pressure, P t is the atmospheric profile top pressure, q υ is the atmospheric humidity information.

[0018] Step 5. Using the reflectance and brightness temperature of the near-infrared and thermal-infrared water vapor channels of the meteorological satellite MODIS and the atmospheric temperature, atmospheric humidity, and pressure profiles of the numerical weather prediction model under the cloud-covered sky conditions simulated in Step 3 as inputs, and the precipitable water in the atmosphere obtained by vertically integrating the atmospheric temperature, atmospheric humidity, and pressure profiles of the atmospheric sounding balloon in Step 4 layer by layer as the output, conduct model training, model verification, and error assessment, and construct an estimation model for the precipitable water in the cloud-covered sky atmosphere using a convolutional neural network;

[0019] Step 6. Use the MODIS cloud detection product to distinguish cloud-covered sky and clear-sky pixels, and then extract the reflectance and brightness temperature of the near-infrared and thermal-infrared water vapor channels actually observed by MODIS under cloud-covered sky conditions. Extract the atmospheric temperature, atmospheric humidity, and pressure profiles of the numerical weather prediction model based on the longitude and latitude of the observed pixels of the meteorological satellite MODIS, and input these data into the estimation model for the precipitable water in the cloud-covered sky atmosphere constructed in Step 5 to estimate the precipitable water in the cloud-covered sky atmosphere.

[0020] Further, 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 recent decades, they are judged as outliers.

[0021] Further, the time and space matching is based on the position and observation time of the atmospheric sounding balloon. Select the profile of the observation grid point of the numerical weather prediction model closest to the position of the atmospheric sounding balloon as the data for space matching, and select the profile of the forecast time of the numerical weather prediction model closest to the observation time of the atmospheric sounding balloon as the data for time matching.

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

[0023] Further, 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] Further, the near-infrared and thermal-infrared water vapor channels of the meteorological satellite MODIS: the 0.89 - 0.96 μm near-infrared water vapor absorption channel of MODIS, the 6.5 - 8.7 μm infrared water vapor absorption channel of MODIS, and the 10.7 - 12.3 μm thermal-infrared water vapor channel of MODIS.

[0025] Further, the MODIS cloud detection product is the cloud cover 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.

[0026] Advantages 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 in the case of a cloudy sky, and uses the atmospheric radiation transfer model MODTRAN to perform radiation simulation calculations on the near-infrared and thermal-infrared water vapor channels of the meteorological satellite in the case of a cloudy sky. Perform stratified vertical integration on the atmospheric profile to obtain the precipitable water in the upper part of the cloud top and the whole layer, and analyze the relationship between the precipitable water in the upper part of the cloud top and the precipitable water in the whole layer. The near-infrared and thermal-infrared water vapor channels can detect the precipitable water in the atmosphere above the cloud, and the numerical weather prediction model can provide the distribution of the atmospheric temperature, atmospheric humidity, and pressure profile of the whole layer. Assume that the form of the atmospheric profile of the numerical weather prediction is correct, but deviation correction is required. Use the radiation of the near-infrared and thermal-infrared water vapor channels, the atmospheric temperature, atmospheric humidity, and pressure profile predicted by the model as inputs, and use a convolutional neural network to construct an estimation model for the precipitable water in the cloudy sky atmosphere. The present invention uses multi-band observation and numerical weather prediction model information, and considers the atmospheric radiation transfer process during modeling, and obtains a higher accuracy for the precipitable water in the cloudy sky atmosphere. Description of the drawings

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

[0029] Figure 2 is the schematic flow chart of calculating the reflectivity and brightness temperature of the near-infrared and thermal-infrared water vapor channels using the radiation transfer model of the present invention. Detailed implementation manners

[0030] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without making creative efforts based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0031] The present invention specifically relates to a method for estimating the precipitable water in cloud and sky atmosphere based on meteorological satellites, including collecting the atmospheric sounding balloon and the atmospheric profile data of the Global Forecast System (GFS) of the National Centers for Environmental Prediction in the United States. Using the relative humidity threshold method for relative threshold cloud detection based on the atmospheric sounding profile, and using the atmospheric radiative transfer model MODTRAN to perform radiative simulation calculations on the near-infrared and thermal-infrared water vapor channels of the Moderate Resolution Imaging Spectroradiometer (MODIS) of meteorological satellites in the case of cloud and sky. The near-infrared and thermal-infrared water vapor channels of meteorological satellite MODIS can detect the precipitable water in the atmosphere above the cloud, and the GFS numerical weather prediction model can provide the temperature, humidity, pressure profile distribution of the whole atmosphere and the total precipitable water in the atmosphere. Assuming that the form of the atmospheric profile of GFS is correct, taking the radiation of the near-infrared and thermal-infrared water vapor channels of meteorological satellite MODIS, the atmospheric temperature, humidity, and pressure profiles predicted by the GFS model as inputs, and using a convolutional neural network to construct an estimation model for the precipitable water in cloud and sky atmosphere.

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

[0033] Step 1. Collect the atmospheric profile data of atmospheric sounding balloons and the atmospheric profile data of the GFS numerical weather prediction model, screen out the outliers of the atmospheric profile data of atmospheric sounding balloons and the atmospheric profile data of the GFS numerical weather prediction model, perform time and space matching on these two types of atmospheric profiles, and form the atmospheric temperature, humidity, pressure profiles of the sounding balloon and the atmospheric temperature, humidity, pressure profiles of the GFS numerical weather prediction model that are matched in time and space;

[0034] Among them, the outlier screening is to judge whether the atmospheric temperature, 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. The time and space matching is based on the position and observation time of the atmospheric sounding balloon, selecting the GFS numerical weather prediction model observation grid point profile closest to the position of the atmospheric sounding balloon as the data for space matching, and selecting the GFS numerical weather prediction model forecast moment profile closest to the observation time of the atmospheric sounding balloon as the data for time matching.

[0035] Step 2. Use the relative humidity threshold method to perform relative threshold cloud detection on the atmospheric temperature, humidity, and pressure profiles collected in Step 1, obtain the vertical cloud distribution structure of the atmosphere, and further form a dataset of atmospheric temperature, humidity, and pressure profiles in the case of cloud and sky that are matched in time and space with the GFS numerical weather prediction model;

[0036] Among them, the relative humidity threshold method is as follows: when the relative humidity is greater than 91% within the altitude range of 0 - 1.0 km, it is judged as cloud; when the altitude is greater than 1.0 km and less than 2.0 km, if the relative humidity is greater than 87%, it is judged as cloud; when the altitude is greater than 2.0 km and less than 7.0 km, if the relative humidity is greater than 82%, it is judged as cloud; when the altitude is greater than 7.0 km and less than 10.0 km, if the relative humidity is greater than 72%, it is judged as cloud; when the altitude is greater than 10.0 km, if 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 dataset of atmospheric temperature, atmospheric humidity, and pressure profiles collected in step 2 under cloudy and overcast conditions, set the characteristic parameters of the meteorological satellite MODIS instrument, solar altitude angle, and satellite observation angle, and conduct simulation calculations of the reflectance and brightness temperature of the near-infrared and thermal-infrared water vapor channels of the meteorological satellite MODIS under cloudy and overcast 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: the 0.89 - 0.96 μm near-infrared water vapor absorption channel of MODIS, the 6.5 - 8.7 μm infrared water vapor absorption channel of MODIS, and the 10.7 - 12.3 μm thermal-infrared water vapor channel of MODIS.

[0039] Step 4. Conduct stratified vertical integration calculations on the atmospheric temperature, atmospheric humidity, and pressure profiles collected in step 2 under cloudy and overcast conditions, and calculate and obtain the total precipitable water in the upper layer of the cloud and the entire atmosphere of the radiosonde and the GFS numerical prediction model.

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

[0041] Integrate the atmospheric temperature, atmospheric humidity, and pressure profiles of the entire atmosphere to obtain the total precipitable water, and integrate the atmospheric profiles above the cloud layer to obtain the precipitable water above the cloud layer.

[0042] Specifically, the calculation is carried out through the following formula:

[0043]

[0044] In the formula, PW is the precipitable water, g is the acceleration due to gravity, P s is the surface air pressure or cloud top pressure, P t is the air pressure at the top of the atmospheric profile, q υ is the atmospheric humidity information.

[0045] Step 5. Take the reflectance and brightness temperature of the near-infrared and thermal-infrared water vapor channels of the meteorological satellite MODIS and the atmospheric temperature, atmospheric humidity, and pressure profiles of the GFS numerical weather prediction model under the simulated cloudy sky conditions in Step 3 as inputs, and take the precipitable water in the atmosphere obtained by vertically integrating the atmospheric temperature, atmospheric humidity, and pressure profiles of the atmospheric sounding balloons in Step 4 layer by layer as the output, and perform model training, model verification, and error assessment, and use a convolutional neural network to construct an estimation model for the precipitable water in the cloudy sky atmosphere.

[0046] Step 6. Use the MODIS cloud detection product to distinguish between cloudy sky and clear sky pixels, and then extract the reflectance and brightness temperature of the near-infrared and thermal-infrared water vapor channels actually observed by MODIS under cloudy sky conditions. Extract the atmospheric temperature, atmospheric humidity, and pressure profiles of the GFS numerical weather prediction model according to the longitude and latitude of the observed pixels of the meteorological satellite MODIS, and input these data into the estimation model for the precipitable water in the cloudy sky atmosphere constructed in Step 5 to estimate the precipitable water in the cloudy sky atmosphere.

[0047] Among them, the MODIS cloud detection product is the cloud cover mask data of each pixel of the meteorological satellite MODIS. Based on this data, it can be judged 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 and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for estimating the precipitable water in cloud and sky atmosphere based on meteorological satellites, characterized in that, Including: Step 1. Collect the atmospheric sounding balloon atmospheric profile data and the numerical weather prediction model atmospheric profile data, screen the outliers from the atmospheric sounding balloon atmospheric profile data and the numerical weather prediction model atmospheric profile data, perform the time and space matching of these two types of atmospheric profiles, and form the temporally and spatially matched atmospheric temperature, atmospheric humidity, pressure profiles of the sounding balloon and the atmospheric temperature, atmospheric humidity, pressure profiles of the numerical weather prediction model; Step 2. Use the relative humidity threshold method to perform relative threshold cloud detection on the atmospheric temperature, atmospheric humidity, and pressure profiles of the atmospheric sounding balloon collected in Step 1, obtain the vertical cloud distribution structure of the atmosphere, and further form the dataset of the atmospheric temperature, atmospheric humidity, and pressure profiles under cloud conditions that are temporally and spatially matched with the numerical weather prediction model; Step 3. Use the mid-spectral resolution atmospheric radiative transfer model MODTRAN and the dataset of the atmospheric temperature, atmospheric humidity, and pressure profiles under cloud conditions collected in Step 2, set the instrument characteristic parameters, solar zenith angle, and satellite viewing angle of the meteorological satellite MODIS, and perform the simulation calculation of the reflectance and brightness temperature of the near-infrared and thermal-infrared water vapor channels of the meteorological satellite MODIS under cloud conditions; Step 4. Perform the stratified vertical integration calculation on the atmospheric temperature, atmospheric humidity, and pressure profiles under cloud conditions collected in Step 2, and calculate the total precipitable water in the upper layer of the cloud and the entire atmosphere of the atmospheric sounding balloon; The stratified vertical integration is: integrate the stratified atmospheric temperature, atmospheric humidity, and pressure profiles to calculate the precipitable water; Integrate the atmospheric temperature, atmospheric humidity, and pressure profiles of the entire atmosphere to obtain the total precipitable water in the entire atmosphere, and integrate the atmospheric profiles above the cloud layer to obtain the precipitable water above the cloud layer; Specifically, the calculation is carried out through the following formula: where PW is the precipitable water in the atmosphere, g is the acceleration of gravity, P s is the surface pressure or the cloud top pressure, P t is the pressure at the top of the atmospheric profile, q υ is the atmospheric humidity information; Step 5. Use the reflectance and brightness temperature of the near-infrared and thermal-infrared water vapor channels of the meteorological satellite MODIS simulated in Step 3, and the atmospheric temperature, atmospheric humidity, and pressure profiles of the numerical weather prediction model as inputs, and the precipitable water obtained from the stratified vertical integration of the atmospheric temperature, atmospheric humidity, and pressure profiles of the atmospheric sounding balloon in Step 4 as outputs, perform model training, model verification, and error assessment, and use a convolutional neural network to construct a cloud atmospheric precipitable water estimation model; Step 6. Use the MODIS cloud detection product to distinguish between cloud and clear sky pixels, and then extract the reflectance and brightness temperature of the near-infrared and thermal-infrared water vapor channels actually observed by MODIS under cloud conditions, extract the atmospheric temperature, atmospheric humidity, and pressure profiles of the numerical weather prediction model according to the longitude and latitude of the observed pixels of the meteorological satellite MODIS, and input these data into the cloud atmospheric precipitable water estimation model constructed in Step 5 to estimate the cloud atmospheric precipitable water.

2. The method for estimating the precipitable water in cloud and sky atmosphere based on meteorological satellites according to claim 1, characterized in that, The outlier screening described in Step 1 is to judge 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 recent decades, they are judged as outliers; The time and space matching is based on the position and observation time of the radiosonde balloon. The profile of the numerical weather prediction model observation grid point closest to the position of the radiosonde balloon is selected as the data for space matching, and the profile of the forecast time of the numerical weather prediction model closest to the observation time of the radiosonde balloon is selected as the data for time matching.

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

4. The method for estimating precipitable water in cloud and sky atmosphere 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 - 0.96 μm near-infrared water vapor absorption channel, 6.5 - 8.7 μm infrared water vapor absorption channel, and 10.7 - 12.3 μm thermal-infrared water vapor channel.

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

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