CN114037023B - Correction method for correcting polar region abnormal water vapor data of microwave radiometer by using ocean satellite - Google Patents
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Abstract
The invention belongs to the technical field of water vapor detection, and relates to a correction method for correcting extremely abnormal water vapor data of a microwave radiometer by using a marine satellite, which comprises the following steps: extracting CMR vapor data points and brightness temperature data of a test area marked as sea ice and land from a CMR vapor product of a sea second satellite (HY-2A); according to the observation time of the CMR water vapor data points, selecting the closest ERA5 PWV data of a certain hour in one day, and carrying out time matching of the data points; determining grid units of ERA5 data where the CMR vapor data points are located according to the position coordinates of the CMR vapor data points, calculating ERA5 PWV at the CMR vapor data points by using ERA5 PWV of four grid points and adopting a bilinear interpolation method to complete space matching of the data points; constructing a correction model of CMR sea ice and land abnormal water vapor; and (5) verifying model accuracy.
Description
Technical Field
The invention belongs to the technical field of water vapor detection, and relates to a correction method for correcting extremely abnormal water vapor data of a microwave radiometer by using a marine satellite.
Background
Atmospheric moisture is one of the determinants affecting weather systems and atmospheric motion, and accurate measurement of its distribution is of great importance for weather analysis and prediction, especially in global warming environments, monitoring of global atmospheric moisture content is of particular importance. The ocean No. two satellites (HaiYang-2A, HY-2A) are the first ocean power environment satellites independently developed in China, and the carried correction microwave radiometers (Calibration Microwave Radiometer, CMR) can realize the monitoring of the global atmosphere water vapor content. HY-2A CMR is a three frequency (18.7 GHz, 23.8GHz, 37.0 GHz) microwave radiometer with a floor footprint of approximately 40km.
The regression model commonly used in CMR water vapor inversion at present is a logarithmic model, and comprises a seven-parameter model and a four-parameter model. The HY-2A CMR water vapor inversion is primarily directed at data identified as ocean, is affected by footprint (about 40 km), and the signals detected by the footprint contain both land or sea ice and ocean signals as they traverse sea-land junctions or sea ice. Because the radiation characteristics of land, sea ice and sea water are inconsistent, the ocean lighting Wen Fanyan model is not applicable to land or sea ice areas, so that the water vapor data marked as sea ice and land are abnormal. Most current studies choose to delete such abnormal water vapor data, but this can result in a significant loss of water vapor data in coastal zones, particularly polar regions.
Sea ice + land abnormal water vapor data are mainly distributed in the antarctic sea area in a 2015 time window 11:30-12:30 are examples, and FIG. 1 shows the results of comparing sea ice+land abnormal water vapor data with the latest ERA5 atmospheric precipitation (Precipitable Water Vapor, PWV) data issued by ECMWF, with the HY-2A CMR having a longitude of 130 to 150 degrees W in the south pole sea area and a latitude of about 70 to 80 degrees S. As can be seen from FIG. 1, the correlation between the two is very poor, only 15.46%, the average deviation reaches 11.81mm, and the inversion model of CMR abnormal water vapor data marked as sea ice and land is inaccurate or inapplicable.
Disclosure of Invention
The invention aims to provide a correction method for extremely abnormal water vapor data of a marine satellite correction microwave radiometer, which aims to solve the problems that in the prior art, the extremely abnormal water vapor data marked as sea ice and land in the extremely sea area of a marine satellite correction microwave radiometer exists and a water vapor inversion model is inaccurate.
A correction method for correcting polar abnormal water vapor data of a microwave radiometer by using a marine satellite comprises the following steps:
S1, extracting CMR vapor data points of sea ice and land marked by a test area from an HY-2A CMR vapor product and brightness temperature data of the CMR vapor data points;
s2, performing space-time matching and calculation on ERA5 PWV and CMR water vapor data;
S3, constructing a correction model of CMR sea ice and land abnormal water vapor;
S4, model accuracy verification.
Preferably, in step S2, ERA5 PWV matching the CMR moisture data point is extracted using bilinear interpolation.
Preferably, step S2 comprises the following sub-steps:
S2.1, according to the observation time of CMR water vapor data, selecting the closest ERA5 PWV data of a certain hour in one day, and performing time matching of CMR water vapor data points;
S2.2, determining the grid unit of ERA5 data where the CMR vapor data point is located according to the position coordinates of the CMR vapor data point;
S2.3, calculating ERA5 PWV at the CMR water vapor data point by using ERA5 PWV of the four grid points and adopting a bilinear interpolation method, and completing space matching of the data points.
Preferably, the bilinear interpolation algorithm of step S2.3 is specifically:
a＝round((round(Lat)+1-Lat)/0.25)+1+(Lat0-round(Lat)-1)/0.25；
b＝round((Lon-round(Lon))/0.25)+1+(round(Lon)-Lon0)/0.25；
y1＝Lat0-0.25*a;y2＝y1+0.25;x2＝b*0.25+Lon0;x1＝x2-0.25;
P1＝PWV(a,b)/(x2-x1)*(y2-y1)*(x2-Lon0)*(Lat0-y1);
P2＝PWV(a,b+1)/(x2-x1)*(y2-y1)*(Lon0-x1)*(Lat0-y1);
P3＝PWV(a+1,b)/(x2-x1)*(y2-y1)*(x2-Lon0)*(y2-Lat0);
P4＝PWV(a+1,b+1)/(x2-x1)*(y2-y1)*(Lon0-x1)*(y2-Lat0);
P＝P1+P2+P3+P4；
The round is a whole symbol, lat is the latitude of the CMR water vapor data point to be solved, lon is the longitude of the CMR water vapor data point to be solved, lat 0 is the latitude of the northwest angle grid point of the test area, lon 0 is the longitude of the northwest angle grid point of the test area, and the unit is an angle; a is the number of rows of grid points, b is the number of columns of grid points; PWV (a, b) represents the moisture value of row a, column b grid points; (x 1,y1)、(x1,y2)、 (x2,y1) and (x 2,y2) are coordinates of four grid points of a grid unit where the CMR vapor data points are to be found; p is ERA5 PWV of the CMR water vapor data point to be solved.
Preferably, the correction model of step S3 is: p=a 0+a18.7TB18.7+a23.8TB23.8+a37.0TB37.0, P is the atmospheric moisture content; TB 18.7、TB23.8 and TB 37.0 are respectively brightness temperature values measured by three frequencies of HY-2A CMR, and the unit is K; a 0、a18.7、a23.8、a37.0 is a model coefficient.
Preferably, in step S4, the HY-2A CMR bright temperature data is substituted into the correction model to obtain a corrected CMR sea ice+land water vapor value, and then the corrected CMR sea ice+land water vapor value is compared with the matched ERA5 PWV and is subjected to accuracy verification.
Compared with the prior art, the linear regression correction model provided and constructed by the invention has the advantages of simple model, higher precision and the like, and provides a new thought and method for correcting the HY-2A CMR abnormal water vapor data.
Drawings
FIG. 1 is a comparison of pre-calibration CMR sea ice+land moisture with ERA5 PWV;
FIG. 2 is an error sequence of correcting pre-CMR sea ice+land moisture;
FIG. 3 is a comparison of the deviation of CMR sea ice + land moisture from ERA5 PWV before and after correction.
Detailed Description
The invention is described in further detail below in connection with the following detailed description:
A correction method for correcting polar abnormal water vapor data of a microwave radiometer by using a marine satellite comprises the following steps:
S1, extracting CMR vapor data points of sea ice and land marked by a test area and brightness temperature data from a CMR vapor product;
s2, performing space-time matching and calculation on ERA5 PWV and CMR water vapor data;
S3, constructing a correction model of CMR sea ice and land abnormal water vapor;
S4, model accuracy verification.
In step S2, ERA5 PWV matching the CMR water vapor data points is extracted by bilinear interpolation.
Step S2 comprises the following sub-steps:
s2.1, according to the observation time of CMR water vapor data, selecting the closest ERA5 PWV data in one hour in one day, and carrying out time matching of data points;
S2.2, determining the grid unit of ERA5 data where the CMR vapor data point is located according to the position coordinates of the CMR vapor data point;
S2.3, calculating ERA5 PWV at the CMR water vapor data point by using ERA5 PWV of the four grid points and adopting a bilinear interpolation method, and completing space matching of the data points.
The bilinear interpolation algorithm of step S2.3 is specifically:
a＝round((round(Lat)+1-Lat)/0.25)+1+(Lat0-round(Lat)-1)/0.25；
b＝round((Lon-round(Lon))/0.25)+1+(round(Lon)-Lon0)/0.25；
y1＝Lat0-0.25*a;y2＝y1+0.25;x2＝b*0.25+Lon0;x1＝x2-0.25;
P1＝PWV(a,b)/(x2-x1)*(y2-y1)*(x2-Lon0)*(Lat0-y1);
P2＝PWV(a,b+1)/(x2-x1)*(y2-y1)*(Lon0-x1)*(Lat0-y1);
P3＝PWV(a+1,b)/(x2-x1)*(y2-y1)*(x2-Lon0)*(y2-Lat0);
P4＝PWV(a+1,b+1)/(x2-x1)*(y2-y1)*(Lon0-x1)*(y2-Lat0);
P＝P1+P2+P3+P4；
The round is a whole symbol, lat is the latitude of the CMR water vapor data point to be solved, lon is the longitude of the CMR water vapor data point to be solved, lat 0 is the latitude of the northwest angle grid point of the test area, lon 0 is the longitude of the northwest angle grid point of the test area, and the unit is an angle; a is the number of rows of grid points, b is the number of columns of grid points; PWV (a, b) represents the moisture value of row a, column b grid points; (x 1,y1)、(x1,y2)、(x2,y1) and (x 2,y2) are coordinates of four grid points of a grid unit where the CMR vapor data points are to be found; p is ERA5 PWV of the CMR water vapor data point to be solved.
The correction model of step S3 is: p=a 0+a18.7TB18.7+a23.8TB23.8+a37.0TB37.0, P is the atmospheric moisture content; TB 18.7、TB23.8 and TB 37.0 are respectively brightness temperature values measured by three frequencies of HY-2A CMR, and the unit is K; a 0、 a18.7、a23.8、a37.0 is a model coefficient.
An observation equation is established by utilizing the HY-2A CMR bright temperature data of 9 months before 2015 and the matched ERA5 PWV, and the coefficients of the linear regression model are obtained by adopting a least squares method and are shown in table 1.
TABLE 1 CMR coefficient of sea ice+land moisture correction model
In step S4, substituting the HY-2A CMR bright temperature data into the correction model to obtain corrected CMR sea ice and land water vapor values, and comparing and verifying the corrected CMR sea ice and land water vapor values with the matched ERA5 PWV.
FIG. 2 is an error sequence of sea ice+land abnormal water vapor data versus ERA5 PWV for HY-2A CMR in the antarctic sea region. Statistical analysis is performed on the error sequence, and an obvious linear relation exists between the CMR abnormal water vapor data and ERA5 PWV, so that a linear regression model is more suitable for correcting the CMR abnormal water vapor data. The method comprises the steps of taking 130-150 DEG W, 70-80 DEG S and the vicinity of the south pole sea area in 2015 as test areas, taking HY-2A CMR bright temperature data and ERA5 PWV marked as sea ice and land as sample data, establishing a linear regression correction model of CMR sea ice and land abnormal water vapor data by using the data of the first 9 months, and then performing accuracy verification and error analysis of the model by using the data of the last 3 months.
(1) Correction of deviation contrast of CMR sea ice and land water vapor before and after correction
By adopting the linear regression correction model constructed by the invention, the HY-2A CMR light Wen Fanyan of 3 months after 2015 is used for calculating the south pole sea area CMR sea ice and land water vapor. Then, sea ice and land water vapor before and after model correction are respectively differed from the matched ERA5 PWV, so that deviation of CMR water vapor relative to the ERA5 PWV is obtained, and the result pair is shown in FIG. 3.
As can be seen from fig. 3, the CMR water vapor marked as sea ice and land in the antarctic sea area shows strong abnormality before being uncorrected, compared with ERA5 PWV, the deviation between the two is fluctuated around 10mm, the average deviation is 11.65mm, the maximum deviation reaches 35mm, the correlation is poor, and the CMR water vapor inversion model is not suitable for sea ice and land sea area. Compared with ERA5 PWV, the average deviation of the obtained CMR sea ice plus land water vapor is only 0.07mm, and the CMR sea ice plus land water vapor has good consistency.
(2) Precision evaluation of corrected CMR sea ice and land water vapor data
Error statistics is carried out on the deviation of the CMR sea ice and land water vapor data obtained by utilizing the linear regression correction model constructed by the invention relative to ERA5 PWV, and the average deviation, the root mean square error (RMS) and the standard deviation (STD) are used as numerical indexes to carry out precision evaluation on the corrected CMR sea ice and land water vapor data.
Table 2 shows the error statistics of HY-2A CMR sea ice+land water vapor relative to ERA5 PWV after correction by using the linear regression correction model and the conventional logistic regression model. It can be seen that the correction result of the linear regression model achieves an ideal effect, and the average deviation of the corrected CMR sea ice and land water vapor is smaller than 0.1mm, and the consistency with ERA5 PWV is good; STD and RMS are both within 0.8mm, and CMR sea ice+land water vapor data has higher accuracy relative to ERA5 PWV.
TABLE 2 error statistics (mm) for corrected CMR sea ice+land moisture data
It should be understood that the above description is not intended to limit the invention to the particular embodiments disclosed, but to limit the invention to the particular embodiments disclosed, and that the invention is not limited to the particular embodiments disclosed, but is intended to cover modifications, adaptations, additions and alternatives falling within the spirit and scope of the invention.
Claims (4)
1. The method for correcting the polar anomaly water vapor data of the microwave radiometer by using the marine satellite is characterized by comprising the following steps of:
S1, extracting CMR vapor data points of sea ice and land marked by a test area from an HY-2A CMR vapor product and brightness temperature data of the CMR vapor data points;
s2, performing space-time matching and calculation on ERA5 PWV and CMR water vapor data points;
Step S2 comprises the following sub-steps:
S2.1, according to the observation time of CMR water vapor data, selecting the closest ERA5 PWV data of a certain hour in one day, and performing time matching of CMR water vapor data points;
S2.2, determining the grid unit of ERA5 data where the CMR vapor data point is located according to the position coordinates of the CMR vapor data point;
s2.3, calculating ERA5 PWV at the CMR water vapor data point by using ERA5 PWV of four grid points and adopting a bilinear interpolation method to complete space matching of the CMR water vapor data point;
S3, constructing a correction model of CMR sea ice and land abnormal water vapor;
The correction model of step S3 is: p=a 0+a18.7TB18.7+a23.8TB23.8+a37.0TB37.0, P is the atmospheric moisture content; TB 18.7、TB23.8 and TB 37.0 are respectively brightness temperature values measured by three frequencies of HY-2A CMR, and the unit is K; a 0、a18.7、a23.8、a37.0 is a model coefficient;
S4, model accuracy verification.
2. The method for correcting polar anomaly moisture data for a marine satellite corrected microwave radiometer of claim 1, wherein in step S2, a bilinear interpolation is used to extract ERA5 PWV matching the CMR moisture data points.
3. The method for correcting polar anomaly water vapor data of a marine satellite corrected microwave radiometer according to claim 2, wherein the bilinear interpolation algorithm of step S2.3 is specifically:
a＝round((round(Lat)+1-Lat)/0.25)+1+(Lat0-round(Lat)-1)/0.25；
b＝round((Lon-round(Lon))/0.25)+1+(round(Lon)-Lon0)/0.25；
y1＝Lat0-0.25*a;y2＝y1+0.25;x2＝b*0.25+Lon0;x1＝x2-0.25;
P1＝PWV(a,b)/(x2-x1)*(y2-y1)*(x2-Lon0)*(Lat0-y1);
P2＝PWV(a,b+1)/(x2-x1)*(y2-y1)*(Lon0-x1)*(Lat0-y1);
P3＝PWV(a+1,b)/(x2-x1)*(y2-y1)*(x2-Lon0)*(y2-Lat0);
P4＝PWV(a+1,b+1)/(x2-x1)*(y2-y1)*(Lon0-x1)*(y2-Lat0);
P＝P1+P2+P3+P4；
The round is a whole symbol, lat is the latitude of the CMR water vapor data point to be solved, lon is the longitude of the CMR water vapor data point to be solved, lat 0 is the latitude of the northwest angle grid point of the test area, lon 0 is the longitude of the northwest angle grid point of the test area, and the unit is an angle; a is the number of rows of grid points, b is the number of columns of grid points; PWV (a, b) represents the moisture value of row a, column b grid points; (x 1,y1)、(x1,y2)、(x2,y1) and (x 2,y2) are coordinates of four grid points of a grid unit where the CMR vapor data points are to be found; p is ERA5 PWV of the CMR water vapor data point to be solved.
4. The method for correcting polar anomaly water vapor data of a marine satellite corrected microwave radiometer according to claim 3, wherein in step S4, the HY-2A CMR bright temperature data is substituted into the correction model to obtain corrected CMR sea ice+land water vapor value, and then the corrected CMR sea ice+land water vapor value is compared with the matched ERA5 PWV and the accuracy is verified.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| RU2015154086A (en) * | 2015-12-16 | 2017-06-21 | Федеральное государственное бюджетное учреждение "Арктический и антарктический научно-исследовательский институт" | METHOD FOR ESTIMATING RAIN INTENSITY BY MEASUREMENT DATA OF THE SATELLITE MICROWAVE RADIOMETER AMSR2 |
| CN109633573A (en) * | 2018-10-24 | 2019-04-16 | 上海无线电设备研究所 | A kind of bearing calibration to moist convection layer delay time error in substantially wide scope |
| CN109709558A (en) * | 2019-03-06 | 2019-05-03 | 成都信息工程大学 | A kind of physics inversion algorithm of satellite-borne microwave remote sensing Over-land PWV |
| WO2020230501A1 (en) * | 2019-05-13 | 2020-11-19 | 古野電気株式会社 | Water vapor observation system and water vapor observation method |
| CN112414554A (en) * | 2020-12-02 | 2021-02-26 | 国家卫星海洋应用中心 | Sea surface salinity obtaining method, device, equipment and medium |
| CN113127794A (en) * | 2021-04-07 | 2021-07-16 | 中山大学 | Method for calculating density of arctic sea ice |
-
2021
- 2021-12-20 CN CN202111558702.8A patent/CN114037023B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| RU2015154086A (en) * | 2015-12-16 | 2017-06-21 | Федеральное государственное бюджетное учреждение "Арктический и антарктический научно-исследовательский институт" | METHOD FOR ESTIMATING RAIN INTENSITY BY MEASUREMENT DATA OF THE SATELLITE MICROWAVE RADIOMETER AMSR2 |
| CN109633573A (en) * | 2018-10-24 | 2019-04-16 | 上海无线电设备研究所 | A kind of bearing calibration to moist convection layer delay time error in substantially wide scope |
| CN109709558A (en) * | 2019-03-06 | 2019-05-03 | 成都信息工程大学 | A kind of physics inversion algorithm of satellite-borne microwave remote sensing Over-land PWV |
| WO2020230501A1 (en) * | 2019-05-13 | 2020-11-19 | 古野電気株式会社 | Water vapor observation system and water vapor observation method |
| CN112414554A (en) * | 2020-12-02 | 2021-02-26 | 国家卫星海洋应用中心 | Sea surface salinity obtaining method, device, equipment and medium |
| CN113127794A (en) * | 2021-04-07 | 2021-07-16 | 中山大学 | Method for calculating density of arctic sea ice |
Non-Patent Citations (2)
| Title |
|---|
| 利用海洋(H Y -2)卫星微波辐射计数据反演北极区域海冰密集度;石立坚;王其茂;邹斌;施英妮;焦敏;极地研究;20141231;第26卷(第4期);全文 * |
| 吉林地区非线性大气加权平均温度模型;邹玉学;岳迎春;叶涛;李振;;导航定位学报;20200817(第04期);全文 * |
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