技术领域technical field
本发明涉及云高测量领域，具体为一种基于半导体激光云高仪的云高自动反演方法。The invention relates to the field of cloud height measurement, in particular to an automatic cloud height retrieval method based on a semiconductor laser ceilometer.
背景技术Background technique
云是悬浮在大气中的小水滴、过冷水滴、冰晶或它们的混合物组成的可见聚合物；有时也包含一些较大的雨滴、冰晶和雪晶，其底部不接触地面。云的生成、外形特征、云量、云的分布及其演变不仅反映了当时大气的运动、稳定程度和水汽状况，同时预示着未来天气变化。云层厚度对温室效应有着重要影响，云层的性质和天气特征分析对人工增雨抗旱减灾有着重要指导意义，云层信息尤其云高参数是影响飞机起飞、着陆、飞行安全的重要气象要素之一。如何方便有效地准确确定云层高度并监测其变化过程，对天气系统发展、降雨形成和大气辐射传输模式研究具有十分重要意义。Clouds are visible aggregates of small water droplets, supercooled water droplets, ice crystals, or mixtures thereof suspended in the atmosphere; sometimes also containing some larger raindrops, ice crystals, and snow crystals, the bases of which do not touch the ground. The formation, shape characteristics, cloud amount, cloud distribution and evolution of clouds not only reflect the movement, stability and water vapor conditions of the atmosphere at that time, but also predict future weather changes. Cloud thickness has an important impact on the greenhouse effect. Analysis of cloud properties and weather characteristics has important guiding significance for artificial rainfall enhancement, drought resistance and disaster reduction. Cloud information, especially cloud height parameters, is one of the important meteorological elements that affect aircraft takeoff, landing, and flight safety. How to conveniently and effectively determine the cloud height accurately and monitor its change process is of great significance to the research of weather system development, rainfall formation and atmospheric radiation transfer mode.
目前对云高的测量主要是依赖于目测，要比较准确的判断、辨别各种云的高度，需要掌握云的外形特征、了解云的物理过程，对观测人员业务知识要求高，并且人为误差较大。激光云高仪采用激光原理和微弱信号检测技术有效提取云和大气的后向散射信号，能够实时提取云高的信息，具有智能、便携、实时和动态范围大等优点。基于半导体激光云高仪的反演主要有微分零交叉法、阈值法。微分零交叉法对大气和云的后向散射信号微分，寻找微分信号零点位置，零点位置对应云峰的高度，但是，在复杂的大气环境下气溶胶的干扰将会产生很多过零点，并且白噪声的抖动也会引入额外的过零点，这势必对过零点的有效提取造成很大的影响；阈值法是首先选择一个适当的阈值，以超过设定阈值作为判断基准，阈值的选取基于经验的方法，而实际的大气是剧烈变化的，白天和夜晚的噪声干扰差别明显，因此，基于经验值的阈值法很难适应于复杂的大气环境，白噪声和气溶胶有时也会产生超过设定阈值的奇异值，阈值法没有对奇异值作进一步的处理和筛选，这也将在云高的反演中带入一定的误差。At present, the measurement of cloud height mainly relies on visual observation. To judge and distinguish the height of various clouds more accurately, it is necessary to master the shape characteristics of clouds and understand the physical process of clouds, which requires high professional knowledge of observers and relatively large human errors big. The laser ceilometer adopts laser principle and weak signal detection technology to effectively extract the backscattering signals of clouds and the atmosphere, and can extract cloud height information in real time. It has the advantages of intelligence, portability, real-time and large dynamic range. The inversion based on semiconductor laser ceilometer mainly includes differential zero-crossing method and threshold method. The differential zero-crossing method differentiates the backscattering signals of the atmosphere and clouds, and finds the zero point position of the differential signal. The zero point position corresponds to the height of the cloud peak. Noise jitter will also introduce additional zero-crossing points, which will inevitably have a great impact on the effective extraction of zero-crossing points; the threshold method is to first select an appropriate threshold, and use the threshold as the judgment criterion. The selection of the threshold is based on experience However, the actual atmosphere changes drastically, and the noise interference between day and night is significantly different. Therefore, the threshold method based on empirical values is difficult to adapt to complex atmospheric environments. White noise and aerosols sometimes produce noise that exceeds the set threshold. Singular values, the threshold method does not further process and screen the singular values, which will also bring a certain error in the inversion of cloud height.
发明内容Contents of the invention
本发明要解决的技术问题是：克服现有技术的不足，提供一种基于半导体激光云高仪的云高自动反演方法，以解决传统方法中云高观测人为误差较大，受气溶胶和白噪声干扰大，鉴别能力不高，自适应性不强等问题。The technical problem to be solved in the present invention is: to overcome the deficiencies of the prior art, to provide a cloud height automatic inversion method based on a semiconductor laser ceilometer, to solve the problem of large human error in cloud height observation in the traditional method, which is affected by aerosol and white Large noise interference, low identification ability, poor adaptability and other problems.
为达到上述目的，本发明所采用的技术方案为：In order to achieve the above object, the technical scheme adopted in the present invention is:
一种基于半导体激光云高仪的云高自动反演方法，其特征在于实现步骤如下：A kind of cloud height automatic inversion method based on semiconductor laser ceilometer, it is characterized in that realization steps are as follows:
(1)半导体激光云高仪接收1064nm后向散射回波信号；(1) The semiconductor laser ceilometer receives the 1064nm backscatter echo signal;
(2)由1064nm后向散射回波信号特征获取分段高度；(2) Obtain the segment height from the 1064nm backscatter echo signal feature;
(3)对采集到的1064nm后向散射回波信号进行距离平方修正，并将距离平方修正后的信号取名为PRR信号，分别对1064nm后向散射回波信号和PRR信号进行归一化处理；(3) Perform distance square correction on the collected 1064nm backscatter echo signal, and name the signal after distance square correction as PRR signal, and normalize the 1064nm backscatter echo signal and PRR signal respectively ;
(4)根据分段信号分布特征获取滑动窗口积分阈值以及云顶、云底、云峰、云厚度和云幅度变化阈值；(4) Obtain the sliding window integral threshold and the cloud top, cloud base, cloud peak, cloud thickness and cloud amplitude change threshold according to the segmental signal distribution characteristics;
(5)采用滑动窗口积分的方法，从地面开始向上逐一滑动窗口，在窗口内对分段信号进行累加平均，当信号累加平均值大于等于滑动窗口积分阈值时，初步筛选出云层分布的大致位置；(5) Using the method of sliding window integration, sliding the window upward one by one from the ground, and accumulating and averaging the segmented signals within the window, when the accumulated average of the signals is greater than or equal to the sliding window integration threshold, the approximate location of the cloud layer distribution is preliminarily screened out ;
(6)由分段信号分布特征确定云底、云峰、云顶的精确高度；(6) Determine the precise height of the cloud base, cloud peak, and cloud top by the segmental signal distribution characteristics;
(7)利用云厚度阈值、云峰阈值、云幅度变化阈值对云层进行进一步的筛选，当经过(6)步骤确定的云厚、云变化幅度、云峰的幅度其中两项同时大于等于对应的阈值时，即筛选过后的云高分布信息。(7) Use the cloud thickness threshold, cloud peak threshold, and cloud amplitude change threshold to further screen the cloud layer. When two of the cloud thickness, cloud change amplitude, and cloud peak amplitude determined in (6) are greater than or equal to the corresponding When the threshold is reached, it is the filtered cloud height distribution information.
本发明与现有技术相比的有益效果：The beneficial effect of the present invention compared with prior art:
(1)本发明通过选取背景信号计算出标准偏差，以此为基准对1064nm后向散射回波信号斜率进行自动化判别，有效识别分段高度，并利用分段高度对1064nm后向散射回波信号和PRR信号分段处理，有利于阈值的获取和设定。(1) The present invention calculates the standard deviation by selecting the background signal, and based on this, automatically distinguishes the slope of the 1064nm backscattered echo signal, effectively identifies the segment height, and utilizes the segment height to analyze the 1064nm backscatter echo signal And the PRR signal is processed in segments, which is beneficial to the acquisition and setting of the threshold.
(2)本发明可以在复杂的大气环境条件下，有效减小气溶胶和白噪声在云高分布测量中的干扰，自适应地确定云高识别阈值，高准确性地提取云高分布信息。(2) The present invention can effectively reduce the interference of aerosol and white noise in cloud height distribution measurement under complex atmospheric environment conditions, adaptively determine the cloud height identification threshold, and extract cloud height distribution information with high accuracy.
(3)本发明计算便于程序化，可以长时间在线高时间分辨率和高空间分辨率的对云高分布进行主动遥感测量，这是传统仪器所无法实现的。(3) The calculation of the present invention is easy to program, and the active remote sensing measurement of cloud height distribution can be carried out online for a long time with high temporal resolution and high spatial resolution, which cannot be realized by traditional instruments.
附图说明Description of drawings
图1为本发明方法实现的流程图；Fig. 1 is the flowchart that the inventive method realizes;
图2为1064nm后向散射回波信号和PRR信号；Figure 2 is the 1064nm backscattered echo signal and PRR signal;
图3为基于云高自动反演方法的云高仪云高自动监测软件；Fig. 3 is the cloud height automatic monitoring software based on the cloud height automatic inversion method of the ceilometer;
图4黑点代表了云层时空分布演化图。The black dots in Figure 4 represent the evolution map of the cloud layer distribution in space and time.
具体实施方式detailed description
根据本发明中提出的一种基于半导体激光云高仪的云高自动反演方法，以某次实验数据为例进行分析，所述方法步骤为：According to a kind of cloud height automatic inversion method based on semiconductor laser ceilometer proposed in the present invention, take a certain experimental data as an example to analyze, the method steps are:
(1)半导体激光云高仪接收1064nm后向散射回波信号，激光云高仪的1064nm后向散射回波信号可表示为：(1) The semiconductor laser ceilometer receives the 1064nm backscatter echo signal, and the 1064nm backscatter echo signal of the laser ceilometer can be expressed as:
其中，P(λ,z)为波长λ，距离z处的激光云高仪1064nm后向散射回波信号，C为系统常数，β(λ,z)为气体分子和云或气溶胶的总的后向散射系数，α(λ,z)为气体分子和云或气溶胶的消光系数。由于云的后向散射系数远大于气溶胶，所以当激光在大气中传输与云产生后向散射作用时，P(λ,z)将会突然增大，出现突变，呈现出周围气溶胶回波信号截然不同的分布特征，主要表现在：云层回波信号幅度较大；云层回波信号变化幅度大，因此云层内部标准偏差较气溶胶回波信号标准偏差明显偏大；云层回波信号脉冲宽度宽，而干扰噪声具有随机性，脉冲很窄；云层内部可能剧烈变化，表现为云峰出现多个，但认为同一层云。因此，针对云层回波信号独特的特征设定六个阈值，针对云层回波信号幅度，设定滑动窗口积分阈值和云峰阈值；针对云层回波变化幅度，设定云幅度变化阈值；针对云层回波信号宽度，设定云厚度阈值；为了有效识别出云底、云顶信息设定云底、云顶阈值；以六个阈值为判断依据从而由1064nm后向散射回波信号中筛选出云层信息。Among them, P(λ,z) is the wavelength λ, the 1064nm backscattered echo signal of the laser ceilometer at the distance z, C is the system constant, β(λ,z) is the total of gas molecules and clouds or aerosols The backscattering coefficient, α(λ,z) is the extinction coefficient of gas molecules and clouds or aerosols. Since the backscattering coefficient of the cloud is much larger than that of the aerosol, when the laser transmits in the atmosphere and the backscattering effect of the cloud occurs, P(λ,z) will suddenly increase, and there will be a sudden change, showing the echo of the surrounding aerosol The completely different distribution characteristics of the signal are mainly manifested in: the amplitude of the cloud echo signal is large; the variation of the cloud echo signal is large, so the internal standard deviation of the cloud layer is obviously larger than the standard deviation of the aerosol echo signal; the pulse width of the cloud echo signal Wide, while the interference noise is random and the pulse is very narrow; the interior of the cloud layer may change drastically, showing that there are multiple cloud peaks, but it is considered to be the same layer of clouds. Therefore, six thresholds are set according to the unique characteristics of the cloud echo signal, and the sliding window integral threshold and the cloud peak threshold are set for the cloud echo signal amplitude; the cloud amplitude change threshold is set for the cloud echo change amplitude; Echo signal width, set cloud thickness threshold; in order to effectively identify cloud base and cloud top information, set cloud base and cloud top threshold; use six thresholds as judgment basis to filter out cloud information from 1064nm backscatter echo signal.
(2)由1064nm后向散射回波信号特征获取分段高度，选取12km～15km的1064nm后向散射回波信号作为背景信号，计算背景信号的标准偏差；由近地面高度向上将1064nm后向散射回波信号与背景信号的标准偏差逐一进行比较，当两者比值第一次小于等于1时所对用的高度为分段高度；(2) Obtain the section height from the characteristics of the 1064nm backscattered echo signal, select the 1064nm backscattered echo signal from 12km to 15km as the background signal, and calculate the standard deviation of the background signal; The standard deviation of the echo signal and the background signal is compared one by one, and when the ratio of the two is less than or equal to 1 for the first time, the height used is the segment height;
(3)受气溶胶干扰和几何因子的影响，1064nm后向散射回波信号在分段高度以下呈现先上升后下降的变化特征，不利于阈值的选取和设定，而1064nm后向散射回波信号经过距离平方修正后的PRR信号平整形较好，有利于阈值的选取和设定，所以首先对1064nm后向散射回波信号进行距离平方修正、归一化处理、三角滤波后得到PRR信号，如下所示：(3) Affected by aerosol interference and geometric factors, the 1064nm backscattered echo signal shows a change characteristic of rising first and then falling below the segment height, which is not conducive to the selection and setting of the threshold, while the 1064nm backscattered echo signal The PRR signal after distance square correction is better in flat shaping, which is conducive to the selection and setting of the threshold. Therefore, firstly, the 1064nm backscatter echo signal is subjected to distance square correction, normalization processing, and triangular filtering to obtain the PRR signal, as follows Shown:
利用PRR信号确定六个阈值；在此高度以上的1064nm后向散射回波信号随距离变化不明显，而PRR受背景噪声影响，信号抖动随距离增加而加剧，因此直接利用归一化处理后的1064nm后向散射回波信号确定六个阈值。Use the PRR signal to determine six thresholds; the 1064nm backscatter echo signal above this height does not change significantly with the distance, but the PRR is affected by the background noise, and the signal jitter increases with the increase of the distance, so the normalized The 1064nm backscattered echo signal determines six thresholds.
(4)扣除奇异点后，求分段信号的平均值和标准偏差作为滑动窗口积分阈值以及云顶、云底、云峰、云厚度和云幅度变化阈值的选择基准，以此基准为基础乘以是适当的系数作为阈值。所述分段信号在分段高度以下为PRR信号，在分段高度以上为归一化处理后的1064nm后向散射回波信号；所述滑动窗口积分阈值设为分段信号平均值加上标准偏差的2～5倍；所述云顶、云底阈值相同，并设为分段信号平均值加上标准偏差的1.2～2倍；所述云峰阈值设为分段信号平均值加上标准偏差的8～10倍；所述云厚阈值设为100m；所述云幅度变化阈值设为分段信号标准偏差的5～7倍。(4) After deducting the singular points, calculate the average value and standard deviation of the segmented signal as the selection benchmark for the sliding window integration threshold and the cloud top, cloud base, cloud peak, cloud thickness and cloud amplitude change threshold, and multiply this benchmark by is an appropriate coefficient as a threshold. The segmented signal is a PRR signal below the segmented height, and is a normalized 1064nm backscattered echo signal above the segmented height; the sliding window integration threshold is set as the segmented signal average value plus the standard 2 to 5 times of the deviation; the cloud top and cloud base thresholds are the same, and are set as 1.2 to 2 times the average value of the segmented signal plus the standard deviation; the cloud peak threshold is set as the average value of the segmented signal plus the standard deviation 8 to 10 times; the cloud thickness threshold is set to 100m; the cloud amplitude change threshold is set to 5 to 7 times the standard deviation of the segmented signal.
(5)采用滑动窗口积分的方法，将窗口内的分段信号进行累加平均，白噪声将在累积的过程中大部分会自动消除，得到较为明显的云层信息，从而将其与噪声区别开来。所述滑动窗口积分法窗口宽度设定为75m，激光云高仪1064nm后向散射回波信号空间分辨率为7.5m，则为10个点，滑动窗口宽度可以改变，一般可设置在50m～150m范围内；所述滑动窗口积分法由地面开始向上逐一滑动对信号进行累加平均，当信号累加平均值大于等于滑动窗口积分阈值时，初步筛选出云层分布的大致位置。(5) Using the method of sliding window integration, the segmented signals in the window are accumulated and averaged, and most of the white noise will be automatically eliminated during the accumulation process to obtain more obvious cloud information, thereby distinguishing it from noise . The window width of the sliding window integration method is set to 75m, and the spatial resolution of the 1064nm backscattered echo signal of the laser ceilometer is 7.5m, which is 10 points. The sliding window width can be changed, and generally can be set at 50m～150m Within the range; the sliding window integration method starts from the ground and slides upward one by one to accumulate and average the signals. When the signal accumulation average is greater than or equal to the sliding window integration threshold, the approximate position of the cloud layer distribution is initially screened out.
(6)初步筛选出云层分布后，在对应的窗口范围内寻找信号峰值，由于窗口宽度可能小于云层厚度，因此所述信号峰值不一定对应于云峰的高度，由信号峰值对应高度向下寻找，当分段信号大于等于云底阈值时，即为初步筛选出的云底高度；由信号峰值对应高度向上寻找，当分段信号大于等于云顶阈值时，即为初步筛选出的云顶高度；在云底和云顶之间信号的最大值所对应的高度即为云峰高度；云层厚度即为云顶和云底之间的距离。云底和云顶之间分段信号的标准偏差即为云变化幅度。(6) After preliminarily screening out the cloud layer distribution, search for the signal peak value within the corresponding window range. Since the window width may be smaller than the cloud layer thickness, the signal peak value does not necessarily correspond to the height of the cloud peak, and search downwards from the corresponding height of the signal peak value , when the segmented signal is greater than or equal to the cloud base threshold, it is the initially screened cloud base height; search upwards from the height corresponding to the signal peak value, and when the segmented signal is greater than or equal to the cloud top threshold, it is the initially screened cloud top height; The height corresponding to the maximum value of the signal between the cloud base and the cloud top is the height of the cloud peak; the thickness of the cloud layer is the distance between the cloud top and the cloud base. The standard deviation of the segmented signal between cloud base and cloud top is the magnitude of the cloud change.
(7)利用云厚度阈值、云峰阈值、云幅度变化阈值对云层进行进一步的筛选，当经过(6)步骤确定的云厚、云变化幅度、云峰的幅度其中两项同时大于等于对应的阈值时，即筛选过后的云高分布信息。(7) Use the cloud thickness threshold, cloud peak threshold, and cloud amplitude change threshold to further screen the cloud layer. When two of the cloud thickness, cloud change amplitude, and cloud peak amplitude determined in (6) are greater than or equal to the corresponding When the threshold is reached, it is the filtered cloud height distribution information.
如图2所示为1064nm后向散射回波信号和PRR信号，由图中可以看出，在1.7km以下，1064nm后向散射回波信号呈现先上升后下降的变化特征，不具有平整形，不利于阈值选取；而PRR信号具有较好的平整形，1.7km以上的1064nm后向散射回波信号随距离变化不明显，而PRR受背景噪声影响，信号抖动随距离增加而加剧。因此以分段高度为界限分别采用PRR信号和1064nn后向散射回波信号特征对云层信息进行识别。Figure 2 shows the 1064nm backscattered echo signal and PRR signal. It can be seen from the figure that below 1.7km, the 1064nm backscattered echo signal presents a change characteristic of rising first and then falling, without flat shaping. It is not conducive to threshold selection; while the PRR signal has a good flat shape, the 1064nm backscatter echo signal above 1.7km does not change significantly with the distance, and the PRR is affected by the background noise, and the signal jitter increases with the increase of the distance. Therefore, the cloud layer information is identified by using the PRR signal and the 1064nn backscatter echo signal characteristics with the segment height as the boundary.
如图2所示为基于云高自动反演方法的云高仪云高自动监测软件。Figure 2 shows the ceilometer cloud height automatic monitoring software based on the cloud height automatic inversion method.
如图3所示黑点代表了云层时空分布演化图，云层主要分布在4.8km～7.2km范围内，变化特别剧烈，其中2013年9月26日21:时43分39秒云层呈现多层结构，经过云高自动监测软件反演出的云底高度分别为4.55km、4.84km、5.46km、5.71km、6.45km、7.09km，表现为六层云。As shown in Figure 3, the black dots represent the evolution map of the temporal and spatial distribution of clouds. The clouds are mainly distributed in the range of 4.8km to 7.2km, and the changes are particularly drastic. Among them, the clouds at 21:43:39 on September 26, 2013 showed a multi-layer structure , the cloud base heights inverted by cloud height automatic monitoring software are 4.55km, 4.84km, 5.46km, 5.71km, 6.45km, 7.09km respectively, showing hexalayer clouds.
本发明未详细阐述部分属于本领域公知技术。Parts not described in detail in the present invention belong to the well-known technology in the art.
显然，本发明的上述实施例仅是为清楚地说明本发明所作的举例，而并非是对本发明的实施方式的限定。对于所属领域的普通技术人员来说，在上述说明的基础上还可以做出其它不同形式的变化或变动。这里无法对所有的实施方式予以穷举。凡是属于本发明的技术方案所引伸出的显而易见的变化或变动仍处于本发明的保护范围之列。Apparently, the above-mentioned embodiments of the present invention are only examples for clearly illustrating the present invention, rather than limiting the implementation of the present invention. For those of ordinary skill in the art, on the basis of the above description, other changes or changes in different forms can also be made. All the implementation manners cannot be exhaustively listed here. All obvious changes or variations derived from the technical solutions of the present invention are still within the protection scope of the present invention.
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| CN201510354483.XACN104991260B (en) | 2015-06-24 | 2015-06-24 | A kind of cloud level automatic inversion method based on LD cloud telemter |
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| CN201510354483.XACN104991260B (en) | 2015-06-24 | 2015-06-24 | A kind of cloud level automatic inversion method based on LD cloud telemter |
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