CN114608708B - Method for evaluating long-time sequence stability of brightness temperature data of microwave radiometer - Google Patents
Method for evaluating long-time sequence stability of brightness temperature data of microwave radiometer Download PDFInfo
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Abstract
The invention relates to the technical field of passive microwave remote sensing. Aiming at the problems that the influence factors of the brightness temperature drift of a microwave radiometer are more, and the brightness temperature drift amount is difficult to calculate accurately by the traditional method, the invention provides a method for evaluating the stability of the brightness temperature data of the microwave radiometer in a long-time sequence, which comprises the following steps: acquiring sea surface observation brightness temperature data of each channel of the multi-frequency band microwave radiometer according to a certain time scale; forming a frequency histogram from all the bright temperatures of each channel; determining an upper limit threshold of the bright temperature, and selecting all bright temperatures smaller than the threshold to form a cold bright temperature set; using an average value of bright temperatures in which the cold bright temperature concentration is less than n%, and using polynomial fitting to extrapolate bright temperatures corresponding to 0%, namely the coldest bright temperature; the long time series of bright Wen Piaoyi speeds were obtained using straight line fitting. The method avoids the difficulty and complexity of calculating the drift amount from the reason, and can efficiently and accurately evaluate the long-time sequence stability of the bright temperature obtained by the long-time stability of the bright temperature of the microwave radiometer.
Description
Technical Field
The invention belongs to the technical field of passive microwave remote sensing, and particularly relates to a method for evaluating the stability of a long-time sequence of bright temperature data of a microwave radiometer.
Background
The microwave radiometer brightness temperature data can be used for inverting geophysical parameters such as sea surface temperature, wind speed, atmosphere water vapor content, cloud liquid water content and the like. The sea surface temperature is one of the main factors affecting global climate change and long-term weather process, and plays an important role in global water circulation and global surface energy balance. Acquiring a long time series of sea surface temperature changes is of great importance for studying global climate change. The precondition for obtaining the correct sea surface temperature change is that the bright temperature data for inverting the sea surface temperature is stable and has no drift, so that the change of the sea surface temperature is ensured to be the actual change rather than the change caused by the drift of the bright temperature.
The microwave radiometer is a high-sensitivity passive remote sensing load for acquiring the surface radiation characteristics, the imaging process is complex, and the change of any link can possibly cause the drift or error of the brightness temperature measurement of the radiometer. Such as aging of the sensor, changes in satellite attitude, changes in incident angle due to satellite orbit, etc. There is also a normal change or drift caused by a change in the actual geophysical parameters. The stability assessment of the radiometer's bright temperature is to obtain the bright Wen Piaoyi caused by the non-geophysical parameter variation.
Because the brightness temperature drift of the microwave radiometer is often complex and is a change caused by the combined action of a plurality of factors, the contribution of each factor causing the brightness temperature drift is difficult to quantify by the traditional analysis and calculation means, and finally the brightness temperature drift is difficult to evaluate.
Disclosure of Invention
Aiming at the problems that the influence factors of the brightness temperature drift of the microwave radiometer are more, and the traditional method is difficult to calculate the drift amount of the brightness temperature more accurately, the invention provides a method for evaluating the stability of the brightness temperature data of the microwave radiometer in a long-time sequence, and the abnormal change of the brightness temperature is determined according to the self characteristics of the brightness temperature, so that the actual brightness Wen Piaoyi amount is calculated, the difficulty and the complexity of calculating the drift amount from the reason are avoided, and the long-term stability of the brightness temperature of the microwave radiometer can be evaluated efficiently and accurately.
In order to achieve the above object, the method for evaluating the stability of the long-time sequence of the brightness temperature data of the microwave radiometer provided by the invention comprises the following steps:
Step (1) determining a time scale, reading bright temperature observation data of a microwave radiometer according to the time scale, and selecting sea bright temperature observation data without land and sea ice;
Step (2) forming a frequency histogram by using the light temperature data of the microwave radiometer with the time scale selected in the step (1), and determining a light temperature upper limit threshold value of Leng Liang temperature set searching according to the histogram;
Step (3), searching for a cold bright temperature set of each channel in the bright temperature of the whole month obtained in the step (1) according to the bright temperature upper limit threshold value obtained in the step (2);
Step (4) calculating a bright temperature average value corresponding to less than n% in a histogram according to the cold bright temperature set of each channel obtained in the step (3), wherein n is an interval [3,10], the interval between adjacent numerical values in the interval is 0.2, and a vector containing 36 elements is obtained, and each element corresponds to one percentage; the calculation method of the bright temperature average value corresponding to less than n percent comprises the steps of firstly sequencing all bright temperatures, multiplying n percent by the total bright temperature to obtain bright Wen Geshu to be averaged, and taking the bright temperatures of the numbers from small to large from the sequenced bright temperatures to average, namely the bright temperature average value corresponding to less than n percent;
Step (5) performing polynomial fitting by using the vector obtained in the step (4), and extrapolating to obtain a bright temperature corresponding to 0%, namely the coldest bright temperature corresponding to each month;
And (6) performing linear fitting according to the coldest light temperature of each time scale obtained in the step (5) to obtain a linear slope, and then equivalently calculating the light temperature drift speed.
Further, the time scale of step (1) is preferably a month.
Further, the microwave radiometer is preferably a multi-frequency microwave radiometer.
Further, the specific method for determining the upper threshold of the search Leng Liangwen set in the step (2) is to determine according to the bright temperature corresponding to 1/3 of the first peak of the histogram, and the formula is as follows:
TB Upper threshold value ＝1/3·TB First peak of wave
TB First peak of wave is the bright temperature corresponding to the first peak of the histogram; TB Upper threshold value is the upper threshold of the search Leng Liangwen set.
Furthermore, before calculating the bright temperature average value corresponding to less than n%, the step (4) performs ± 3σ control on Leng Liangwen sets to exclude possible abnormal values.
Further, the step (5) may perform a first order polynomial fitting, a second order polynomial fitting, and preferably a third order polynomial fitting; and (3) performing fitting extrapolation calculation by using the vector containing 36 elements obtained in the step (4), wherein the formula is as follows:
TB(f)＝a0+a1f+a2f2+a3f3
f represents 36 percentages of 3% to 10% with 0.2% as interval, TB (f) is 36 average bright temperatures corresponding to the above, the coefficient a 0～a3 is obtained by least square, f=0% is substituted into the above formula according to the obtained a 0～a3, and the bright temperature corresponding to 0%, namely the coldest bright temperature to be solved, can be obtained.
Further, the step (6) is to perform linear fitting on the coldest bright temperature of each month, and the formula is as follows:
y＝slope·x+bias
χ represents the time of the long time series, x's value is greater than or equal to 2, y represents the coldest light temperature corresponding to each time, slope represents the slope of the straight line of the long time series light Wen Nige, bias represents the bias of the straight line of the long time series light Wen Nige, and after fitting the slope and bias values, the equivalent annual Drift velocity Drift year：Driftyear =slope.12 is calculated from the slope.
Since the brightness temperature drift of the microwave radiometer is often complex and is a change caused by the combined action of a plurality of factors, if the drift amount is analyzed and calculated from the reasons of the drift, it is very difficult to quantify the contribution amount of each factor of the brightness temperature drift, and finally, it is also difficult to evaluate the brightness temperature drift amount. The invention starts from the bright temperature itself, and determines the abnormal change of the bright temperature according to the characteristic of the bright temperature itself, so that not only the actual bright Wen Piaoyi quantity can be calculated, but also the difficulty of calculating from the reason is skillfully avoided. The invention is based on the thinking that the coldest bright temperature in a time scale is found according to the histogram distribution characteristic of global offshore bright temperature data in the time scale, and the coldest bright temperature usually exists in a marine area with low sea temperature, low wind speed and low atmospheric vapor, and theoretically the coldest bright temperature does not exist, so that the coldest bright temperature is obtained by searching a cold bright temperature set and fitting by an extrapolation method, and the bright Wen Piaoyi characteristic of a long-time sequence can be obtained according to the characteristic of the coldest bright temperature.
The beneficial effects of the invention are as follows:
1. the invention evaluates the drift condition of the bright temperature based on the bright temperature self characteristic of the microwave radiometer in long time sequence, thereby avoiding the difficulty and complexity from the cause of the bright temperature drift;
2. The method for determining the upper limit threshold value of the coldest bright temperature set has less influence on the results of calculating and extrapolating the coldest bright temperature of different percentages in the subsequent steps, namely the overall robustness of the method is improved;
3. the invention can evaluate the stability of the brightness temperature data of the microwave radiometer with any time length and give out the quantified annual drift speed.
Drawings
FIG. 1 is a flow chart of a method for evaluating the long-time series stability of bright temperature data of a microwave radiometer according to an embodiment of the invention;
FIG. 2 is a histogram of all bright temperatures of channels of an SMR and a bright temperature histogram of channels less than an upper threshold in accordance with an embodiment of the present invention;
FIG. 3 is a schematic diagram of calculating average bright temperatures of different percentages according to an embodiment of the present invention;
FIG. 4 is a graph showing the extrapolation of the bright temperature of the 10.7GHz-H channel to 0% according to the corresponding average bright temperature of 3% -10% in the example of the invention;
FIG. 5 is a graph showing the evaluation result of the bright temperature drift velocity of the 10.7GHz-H channel of the SMR of the embodiment of the invention in 2018, 11 months and 2021, 10 months.
Detailed Description
The present invention will be described in further detail with reference to specific examples.
Examples
In the embodiment, scanning Microwave Radiometer (SMR) multi-frequency bright temperature data of HY-2B satellite is selected and obtained by a national marine satellite data distribution center (https:// osdds. Nsoas. Org. Cn/#). The HY-2B satellite was launched at 10 in 2018, with multiple loads including the SMR mounted thereon. This example evaluates SMR light temperature stability conditions three years (11 th 2018-10 2021) since HY-2B satellite emissions using the techniques described herein.
The theoretical basis of the technical method of the application is as follows: the earth's observation of the radiometer itself provides a natural, reliable calibration standard from which instrument drift can be determined, and the coldest brightness Temperature (TB) observed by an on-board microwave radiometer is often easily modeled and repeatable over time. In other words, in a certain time scale, the frequency histogram formed by global ocean bright temperatures has a stable lower bound, namely the low bright temperature end is stable, and the bright temperature lower bound of the histogram in each time unit is obtained by selecting ocean bright temperatures with certain time length as units, so that bright temperature trend of the units in a long time sequence, namely drift information of the total bright temperature, can be obtained.
As shown in fig. 1, acquiring sea surface observation brightness temperature data of each channel of an SMR based on a data quality identifier, a land identifier and a sea ice identifier by taking a month as a unit; forming a frequency histogram from all the bright temperatures of each channel; determining a bright temperature upper limit threshold according to the frequency histogram, and selecting all bright temperatures smaller than the threshold to form a cold bright temperature set; and (3) forming a bright temperature sequence by using an average value of bright temperatures with the cold bright temperature concentration less than n% (n is more than or equal to 3 and less than or equal to 10 and the interval is 0.2), and extrapolating the bright temperature corresponding to 0% by using 3 times polynomial fitting by using the bright temperature sequence, namely the coldest bright temperature. The coldest bright temperature of each month is obtained, and straight line fitting can be utilized to obtain the bright Wen Piaoyi speed of the long-time sequence.
The specific operation of the method of the present application in the examples for evaluating the bright temperature stability of the SMR is described in detail below.
And (1) reading SMR bright temperature observation data by taking month as a unit, and selecting bright temperature observation data without land or sea ice according to data quality identification, land identification, sea ice identification and the like.
And (2) forming a frequency histogram by using the SMR bright temperature data of the whole month selected in the step (1), and determining an upper limit threshold value of the next cold bright temperature set search according to the histogram. The specific method is that according to the fact that the brightness temperature corresponding to 1/3 of the first peak of the histogram is positive, the formula is as follows:
TB Upper threshold value ＝1/3·TB First peak of wave
TB First peak of wave is the bright temperature corresponding to the first peak of the histogram; TB Upper threshold value is the upper threshold of the search Leng Liangwen set.
And (3) searching for the cold bright temperature set of each channel in the SMR whole month bright temperature obtained in the step (1) according to the upper limit threshold value of the cold bright temperature set search of each channel in the step (2).
Fig. 2 shows a histogram of the total bright temperatures of each channel and a portion (indicated by a bold line) where each channel is smaller than the upper threshold, that is, a cold bright temperature set portion obtained in step (2).
And (4) calculating an average value corresponding to less than n% of bright temperature according to the cold bright temperature set of each channel obtained in the step (3), wherein n is 3 to 10, and the interval is 0.2, so that a vector containing 36 elements can be obtained.
Firstly, 3-sigma control is carried out on the cold and bright temperature set obtained in the step 3, and possible abnormal values are eliminated. The average of all bright temperatures less than n% in the histogram is calculated again. For example, n is 10, namely, all the bright temperatures are sequenced firstly, then the total number of the bright temperatures is multiplied by 10% to obtain bright Wen Geshu K to be averaged, and K bright temperatures are taken from small to large in the sequenced bright temperatures to be averaged, namely, the average bright temperature corresponding to less than 10%. At 0.2% intervals, an average bright temperature of less than 3% to 10% is obtained, i.e. a vector comprising 36 elements, one per element. As shown in fig. 3, the average bright temperatures are calculated for different percentages, such as 10%, i.e. the average of all bright temperatures contained within the black dashed line. Table 1 shows the calculation of the average bright temperature for the different percentages, taking the 10.7GHz-H channel as an example.
Table 1 different percentages and corresponding average bright temperatures (taking 10.7GHz-H as an example)
And (5) performing polynomial fitting for 3 times by using the vector containing 36 elements (shown in table 1) obtained in the step (4), and extrapolating to obtain the bright temperature corresponding to 0%, namely the coldest bright temperature corresponding to each month of the SMR.
The fitting formula is as follows:
TB(f)＝a0+a1f+a2f2+a3f3
f represents 36 percentages less than 3% to 10%, TB (f) is 36 average bright temperatures corresponding to the f, a coefficient a 0～a3 can be obtained by least square, f=0% is substituted into the above formula according to the obtained a 0～a3, and the bright temperature corresponding to 0%, namely the coldest bright temperature to be obtained.
Fig. 4 shows a schematic of extrapolation to 0%.
And (6) performing linear fitting according to the coldest light temperature of the SMR obtained in the step (5) in each month to obtain a linear slope, and equivalently calculating the light Wen Piaoyi speed of the SMR in a long-time sequence.
The linear fitting formula is as follows:
y＝slope·x+bias
χ represents the time of the long time series, χ has a value of 2 or more, y represents the coldest light temperature corresponding to each time, slope represents the slope of the straight line of the long time series light Wen Nige, bias represents the bias of the straight line of the long time series light Wen Nige; after fitting the slope and bias values, wherein from the slope, an equivalent annual drift rate can be calculated:
Driftyear＝slope·12
Drift year is the annual Drift velocity in K/year.
FIG. 5 is an evaluation of the bright temperature drift velocity of the 10.7GHz-H channel of SMR at 11 months 2018 to 10 months 2021.
The above examples are only illustrative of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention, and various modifications and improvements made by those skilled in the art to the technical solution of the present invention should fall within the scope of protection defined by the claims of the present invention without departing from the spirit of the present invention.
Claims (6)
1. A method of evaluating the long-term sequence stability of light-temperature data of a microwave radiometer, the method comprising:
Step (1) determining a time scale, reading bright temperature observation data of a microwave radiometer according to the time scale, and selecting sea bright temperature observation data without land and sea ice;
Step (2) forming a frequency histogram by using the light temperature data of the microwave radiometer with the time scale selected in the step (1), and determining a light temperature upper limit threshold value of Leng Liang temperature set searching according to the histogram;
Step (3), searching for a cold bright temperature set of each channel in the bright temperature of the whole month obtained in the step (1) according to the bright temperature upper limit threshold value obtained in the step (2);
Step (4) calculating a bright temperature average value corresponding to less than n% in a histogram according to the cold bright temperature set of each channel obtained in the step (3), wherein n is an interval [3,10], the interval between adjacent numerical values in the interval is 0.2, and a vector containing 36 elements is obtained, and each element corresponds to one percentage; the calculation method of the bright temperature average value corresponding to less than n percent comprises the steps of firstly sequencing all bright temperatures, multiplying n percent by the total bright temperature to obtain bright Wen Geshu to be averaged, and taking the bright temperatures of the numbers from small to large from the sequenced bright temperatures to average, namely the bright temperature average value corresponding to less than n percent;
Step (5) performing polynomial fitting by using the vector obtained in the step (4), and extrapolating to obtain a bright temperature corresponding to 0%, namely the coldest bright temperature corresponding to each month;
And (6) performing linear fitting according to the coldest light temperature of each time scale obtained in the step (5) to obtain a linear slope, and then equivalently calculating the light temperature drift speed.
2. The method of assessing the stability of a long-term sequence of light and temperature data of a microwave radiometer of claim 1, wherein said step (1) is on a time scale of months.
3. The method for evaluating the long-term sequence stability of light temperature data of a microwave radiometer according to claim 1, wherein the specific method for determining the upper threshold of the search Leng Liangwen set in the step (2) is to determine the light temperature according to 1/3 of the first peak of the histogram, and the formula is as follows:
TB Upper threshold value ＝1/3·TB First peak of wave
TB First peak of wave is the bright temperature corresponding to the first peak of the histogram; TB Upper threshold value is the upper threshold of the search Leng Liangwen set.
4. The method of claim 1, wherein the step (4) performs ± 3σ control on the Leng Liangwen set before calculating the bright temperature average value corresponding to less than n%, and eliminating possible abnormal values.
5. The method for evaluating the long-term sequence stability of light and temperature data of a microwave radiometer according to claim 1, wherein said step (5) specifically comprises:
And (3) performing fitting extrapolation calculation by using the vector containing 36 elements obtained in the step (4), wherein the formula is as follows:
TB(f)＝a0+a1f+a2f2+a3f3
f represents 36 percentages of 3% to 10% with 0.2% as interval, TB (f) is 36 average bright temperatures corresponding to the above, the coefficient a 0～a3 is obtained by least square, f=0% is substituted into the above formula according to the obtained a 0～a3, and the bright temperature corresponding to 0%, namely the coldest bright temperature to be solved, can be obtained.
6. A method of assessing the stability of a long-time series of microwave radiometer bright temperature data according to claim 2 wherein step (6) is a linear fit of the coldest bright temperature for each time scale as follows:
y＝slope·x+bias
χ represents the time of the long time series, χ has a value of 2 or more, y represents the coldest light temperature corresponding to each time, slope represents the slope of the straight line of the long time series light Wen Nige, bias represents the bias of the straight line of the long time series light Wen Nige; after fitting the slope and bias values, an equivalent annual Drift speed Drift year：Driftyear =slope·12 is calculated from the slope.
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| CN112197865A (en) * | 2020-09-02 | 2021-01-08 | 华中科技大学 | Estimation method and system for observation brightness temperature data error of satellite-borne microwave radiometer |
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| CN112197865A (en) * | 2020-09-02 | 2021-01-08 | 华中科技大学 | Estimation method and system for observation brightness temperature data error of satellite-borne microwave radiometer |
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