CN113391376B - Terrestrial cloud detection method for AMSU-A data - Google Patents
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Abstract
The invention provides an AMSU-A data land cloud detection method, which fuses cloud indemutexesA index AndM index the method can eliminate cloud observation of most AMSU-A on land, and the structure and the edge of a cloud system are detected accurately. The method provided by the invention can be applied to a data assimilation system and improves the assimilation of the data of the AMSU-A cloud-influenced channel.
Description
Technical Field
The invention belongs to the technical field of atmospheric science and relates to an AMSU-A data land cloud detection method.
Background
In all satellite data assimilated by a service assimilation system of the current numerical weather forecast, data of an advanced Microwave thermometer (AMSU-A) with the atmospheric vertical detection capability contributes most to reducing forecast errors. The AMSU-A has 15 channels, including 3 window channels (23.8,31.4, and 89.0GHz) for detecting cloud rain and 12 omutexygen absorption channels distributed at 50-60GHz for detecting atmospheric temperature profile. The radiation brightness and temperature observed by the AMSU-A is not a mode variable, so the radiation transmission mode is required for variable conversion. Because the current radiation transmission mode has limited description on the physical process in the cloud, the simulated bright temperature and the observed bright temperature have larger deviation. If the observation data of the cloud is assimilated, the prediction is negatively influenced, so accurate cloud detection is the premise of assimilating AMSU-A data.
Various ocean AMSU-A data cloud detection methods have been proposed at home and abroad, and can be roughly summarized into three types: one is to construct correlation indexes by modeling bright temperature and observing bright temperature (English, s.j.et al, 1997); second, variables based on physical inversion, such as cloud water path (Weng, f.et al.,2003), cloud ice path based on microwave hygrometer window zone channel inversion or cloud products based on other satellite-borne instrument inversion (Qin, z.et al., 2020); and thirdly, training AMSU-A data to carry out cloud detection by using a neural network method (Aires, F.et al.,2011) and observation data of other satellite-borne instruments. Compared with the ocean surface, the earth surface emissivity of the land is closer to the emissivity of the cloud, so most of the cloud detection methods on the ocean surface are not suitable for the land. Only cloud inversion products such as those using other satellite-borne instruments are available, but due to the difference in spatial-temporal resolution, spatial-temporal interpolation is required, which is not economical and convenient in business applications. Based on the AMSU-A data, only a few empirical methods emutexist, and the accuracy of the methods depends on the accuracy of the background field. Therefore, a simple and convenient AMSU-A data terrestrial cloud detection method is needed.
Reference to the literature
English,S.J.；Renshaw,R.J.；Dibben,P.；Eyre,J.R.The AAPP module for identifying precipitation,ice cloud,liquid water and surface type on theAMSU-A grid.Proceeding of the 9th International TOVS Study Conference,Igls,Austria,20–26 February 1997；pp.119–130.
Weng,F.,Zhao,L.,Ferraro,R.R.,Poe,G.,Li,X.,&Grody,N.C.(2003).Advanced microwave sounding unit cloud and precipitation algorithms.Radio Science,38(4),33-1.
Qin,Z.,Wu,Z.,&Li,J.(2020).Impact of the One-Stream Cloud Detection Method on the AssimilationofAMSU-ADatainGRAPES.Remote Sensing,12(22),3842.
Qin,Z.,&Zou,X.(2016).Development and initial assessment of a new land index for microwave humidity sounder cloud detection.Journal ofMeteorological Research,30(1),12-37.
Disclosure of Invention
The invention provides an AMSU-A land data cloud detection method aiming at the problem of difficulty in AMSU-A land data cloud detection caused by the fact that the land surface emissivity is close to the cloud, and based on different response characteristics of different channels of a microwave thermometer and a microwave hygrometer to the cloud, the assimilation effect of a data assimilation system on AMSU-A data is improved.
The technical scheme adopted by the invention is as follows:
an AMSU-A data land cloud detection method comprises the following steps:
step one, selecting observation data of 5 low-layer channels of AMSU-A (advanced Microwave Sounding Unit-A) to construct cloud indemutex AindexThe 5 lower-layer channels are channels 1-4 and 15, and the cloud index AindexThe definition is as follows:
wherein,
Tb,ichannels 1-4 and 15 representing AMSU-A, the observed light temperature of the ith channel of these 5 channels; cloud index AindexIn the middle, the standardized channel 3 brightness temperature is used as a numerator, and the exponentially adjusted channel 15 brightness temperature is used as a denominator;
step two, reading the data of MHS (microwave hub Sound) channels 1-5 carried on the same satellite and at the same time, and constructing a cloud index MindexCloud index MindexThe definition is as follows:
wherein,
Tbm,jrepresents the observed light temperature of the jth channel of the MHS5 channels; cloud index MindexIn the middle, the standardized channel 1 brightness temperature is used as a numerator, and the adjusted channel 2 brightness temperature is used as a denominator;
step three, the cloud index MindexMatching to cloud index AindexC, removing;
and step four, judging whether the observation is positioned above the ground or not according to the longitude and latitude information of each observation, and if the observation field positioned above the ground meets the set conditions, judging that clouds exist in the field.
Further, in step three, 1 AMSU-a field of view corresponds to about 9 MHS fields of view; according to the rule that every 3 microwave hygrometer scanning lines correspond to 1 microwave thermometer scanning line and every 3 microwave hygrometer fields of view on each scanning line correspond to 1 microwave thermometer field of view, M of 9 MHS fields of viewindexAfter summing, averaging, matching to A of 1 AMSU-A field of viewindexThe above.
Further, in step four, the setting conditions are: a. theindex> 0.1 or Mindex＞0.35。
The invention has the beneficial effects that:
the comparison result of the AMSU-A data land cloud detection method and the infrared cloud product provided by the invention shows that the method can eliminate the cloud observation of most AMSU-A on land, the detection of the structure and the edge of a cloud system is more accurate, and the missed detection is only carried out at a small part of cloud rolling positions and scattered point cloud positions. One-month batch emutexperiment results show that most of observations that simulated brightness temperature deviates from observed brightness temperature and is emutexcessive can be eliminated, the | O-B | <10K observed by land of low-layer channels (channels 1-4 and channel 15) in AMSU-A with cloud data is eliminated, and the data distribution is more in accordance with normal distribution. The method provided by the invention can be applied to a data assimilation system and improves the assimilation of the data of the AMSU-A cloud-influenced channel.
Drawings
Fig. 1 is a distribution plot of the detected cloud regions and matching contemporaneous MODIS cloud classification products in northeast asia of 2019, 6, 26, 00, UTC fig. 1(a) and 27, 00, UTC fig. 1(b) AMSU-a cloud indices;
FIG. 2 is a distribution of the field of view of AMSU-A (black circles) and MHS (red circles) mounted on NOAA19 at 00UTC 8/9/2019 in southeast Asia;
fig. 3 is a graph of 2019, 6, 26, 06, UTC, fig. 3(a) and 27, 00, UTC, fig. 3(b) detected cloudy regions of AMSU-a cloud indemutex (black circles) and MHS cloud indemutex (blue circles) and matching contemporaneous MODIS cloud classification product distribution;
fig. 4 is a scatter diagram of bright temperature (horizontal amutemutexis) observed in 2019, 6, 13, 7, 15, NOAA19 AMSU-a channel 3 and simulated bright temperature (vertical amutemutexis), where a blue circle is a cloud observation detected by MHS cloud indemutemutex, a black circle is a cloud area detected by AMSU-a, and a gray circle is a clear sky observation, where three dotted lines represent O-B ═ 10,0, and 10(K), respectively;
FIG. 5 shows the data size distribution in different O-B intervals before cloud observation (left) and after cloud detection (right) in 2019, 6, 13, 7, 15, NOAA19 AMSU-A channels 1-4 and 15, respectively, with color filled as the data size;
in the figure, E and N respectively represent east longitude and north latitude, Clear is Clear sky, Ci is cirque, Cs is convolution cloud, Dc is deep convection cloud, Ac is high-lying cloud, As is high-lying cloud, Ns is raincloud, Cu is rain cloud, Sc is layering cloud, St is layering cloud, and Uncertain represents Uncertain clouds; k represents the Kelvin temperature.
Detailed Description
The invention provides a novel AMSU-A data land cloud detection method by utilizing observed brightness temperatures of AMSU-A and MHS, aiming at solving the problem that the land cloud detection of AMSU-A data in the current assimilation system is difficult.
An AMSU-A data land cloud detection method comprises the following steps:
step one, selecting observation data of 5 low-level channels of AMSU-A to construct cloud indemutex A index5 lower level channels are channels 1-4 and 15, cloud index AindexThe definition is as follows:
wherein,
Tb,irepresenting channels 1-4 and 15 of AMSU-a, the observed light temperature of the ith channel of these 5 channels. Cloud index AindexIn the middle, the normalized channel 3 light temperature is used as a numerator, and the exponentially adjusted channel 15 light temperature is used as a denominator.
Step two, reading the data of MHS channels 1-5 carried on the same satellite and at the same time segment, and constructing a cloud index MindexCloud index MindexThe definition is as follows:
wherein,
Tbm,jindicating the observed light temperature of the jth channel of the MHS5 channels. Cloud index MindexMiddle and standardThe brightness temperature of the channel 1 is used as a numerator, and the brightness temperature of the channel 2 after adjustment is used as a denominator.
Step three, the cloud index MindexMatching to cloud index AindexThe above.
The AMSU-A and the MHS are carried on the same polar orbit satellite, and the time deviation of the AMSU-A and the MHS can be ignored. The width of AMSU-A is about 2226.8 km, the width of MHS is about 2348 km, and the difference between the widths is small. The similar width and observation time can ensure the overlapping relation of the two fields under different scanning angles. The resolution of the subsatellite point of the two instruments is about 48 and 17 kilometers respectively, the resolution of the subsatellite point of the MHS is about 3 times of that of the AMSU-A, 30 and 90 fields of view are respectively arranged on one scanning line, and the field of view equivalent to 1 AMSU-A corresponds to about 9 MHS fields of view. Therefore, according to the rule that every 3 microwave hygrometer scanning lines correspond to 1 microwave thermometer scanning line and every 3 microwave hygrometer fields of view on each scanning line correspond to 1 microwave thermometer field of view, M of 9 MHS fields of viewindexAfter summing, averaging, matching to A of 1 AMSU-A field of viewindexThe above.
Step four, judging whether the observation is positioned above the ground or not according to the longitude and latitude information of each observation, and if the observation field positioned above the ground meets Aindex> 0.1 or MindexIf the current value is more than 0.35, judging that cloud exists in the field of view.
The technical solution of the present invention will be further described with reference to the accompanying drawings and specific examples.
In the emutexample, the data of AMSU-A and MHS carried on NOAA19 from 2019, 6, 13, 7, 15 are selected, and the research area is selected in the northeast Asia to avoid the influence of high terrain.
Calculating AMSU-A cloud indemutex Aindex. According to AindexDefining, selecting the observed brightness temperature data of AMSU-A channels 1-4 and 15, and calculating AindexChoosing the observation on land according to AindexIf the value is more than 0.1, the cloud observation is carried out.
FIG. 1 shows a comparison of cloud regions detected by cloud indemutex of 26/2019/00 UTCAMSU-A and MODIS cloud classification products at the same time, AindexCan detect most cloud areasLike the banded clouds in beigalhu to northeast china in fig. 1(a), the high clouds above the korean peninsula, the low clouds along the sea in the eastern part of china, and the mid-china broad convection clouds in fig. 1 (b). But A isindexThere is also a certain missing detection, the detection of the cloud and the layer cloud is obvious, for example, the detection of the cloud and the layer cloud around the convection cloud system is also not good enough in high weft performance, the detection of the low cloud of the high weft is missed in fig. 1(a), and a small amount of over detection is left in the upper space of the inner Mongolia in fig. 1 (b). A. theindexThe detection precision of the method can not meet the service application level, and in order to improve the problem, MHS cloud indexes are added.
Calculating MHS cloud index MindexThen M is addedindexA matched to corresponding field of viewindexThe above. FIG. 2 shows the distribution of the observation fields of AMSU-A and MHS carried on the same satellite at one moment, and the observation widths of the two instruments are similar. At the sub-satellite point, one AMSU-A field of view, covers 9 MHS fields of view. Thus, according to MindexCalculating M within each MHS field of viewindexThen according to the rule that every 3 MHS scanning lines correspond to 1 AMSU-A scanning line and every 3 MHS fields on each scanning line correspond to 1 AMSU-A field, M of 9 MHS fieldsindexAfter summing, averaging, matching to A of 1 AMSU-A field of viewindexThe above. FIG. 3 shows A at the same time as FIG. 1index(black circles) and M after matchingindex(blue circle) detected cloud areas and a comparison graph of MODIS cloud classification products at the same time. MindexThe addition of (A) well makes up forindexThe problem of missing detection, A in FIG. 3(a)indexThe rolling cloud at the periphery of the convection cloud of the missed detection and the low cloud of the high-latitude missed detection are both processed by MindexAnd when the cloud indexes of the two instruments are detected, the cloud indexes are in parallel flow, most of the cloud observation can be eliminated, the structure and the edge of the cloud system are detected accurately, and the missing detection is only performed at a small part of the rolling cloud and the scattered point cloud. It is worth mentioning that this is a result of comparing infrared cloud products, where infrared is more sensitive to cloud than microwave, and microwave energy penetrates a portion of the thin cloud, so that the portion of the cloud missed by the cloud index may not have any effect on microwave observation.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any alternatives or modifications that can be easily conceived by those skilled in the art within the technical scope of the present invention should be covered within the scope of the present invention.
Claims (1)
1. A land cloud detection method for AMSU-A data is characterized by comprising the following steps:
step one, selecting observation data of 5 low-level channels of AMSU-A to construct cloud indemutex AindexThe 5 lower-layer channels are channels 1-4 and 15, and the cloud index AindexThe definition is as follows:
wherein,
Tb,ichannels 1-4 and 15 representing AMSU-A, the observed light temperature of the ith channel of these 5 channels; cloud index AindexIn the middle, the standardized channel 3 brightness temperature is used as a numerator, and the exponentially adjusted channel 15 brightness temperature is used as a denominator;
step two, reading the data of MHS channels 1-5 carried on the same satellite and at the same time segment, and constructing a cloud index MindexCloud index MindexThe definition is as follows:
wherein,
Tbm,jrepresents the observed light temperature of the jth channel of the MHS5 channels; cloud index MindexIn the middle, the standardized channel 1 brightness temperature is used as a numerator, and the adjusted channel 2 brightness temperature is used as a denominator;
step three, the cloud index MindexMatching to cloud index AindexThe above step (1);
judging whether the observation is located above the ground or not according to the longitude and latitude information of each observation, and if the observation field located above the ground meets the set conditions, judging that clouds exist in the field;
in the third step, the field of view of 1 AMSU-A corresponds to 9 MHS fields of view; according to the rule that every 3 microwave hygrometer scanning lines correspond to 1 microwave thermometer scanning line and every 3 microwave hygrometer fields of view on every scanning line correspond to 1 microwave thermometer field of view, M of 9 MHS fields of viewindexAfter summing, averaging, matching to A of 1 AMSU-A field of viewindexThe above step (1);
in the fourth step, the setting conditions are as follows: a. theindex> 0.1 or Mindex＞0.35。
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| 《Detection of tropical convective clouds from AMSU-B water vapor channels measurements》;Gang Hong;《JOURNAL OF GEOPHYSICAL RESEARCH》;20050315;全文 * |
| 《Impact of the One-Stream Cloud Detection Method on the Assimilation of AMSU-A Data in GRAPES》;Zhengkun Qin;《remote sensing》;20201123;全文 * |
| 《基于亮温通道变率的FY-3C微博波湿度计陆地云检测新方法》;朱利剑;《气象学报》;20180615;全文 * |
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