Assessment of FY-4A and Himawari-8 Cloud Top Height Retrieval through Comparison with Ground-Based Millimeter Radar at Sites in Tibet and Beijing

The accuracy of passive satellite cloud top height (CTH) retrieval shows regional dependence. This paper assesses the CTH derived from the FY-4A and Himawari-8 satellites through comparison with those from the ground-based millimeter radar at two sites: Yangbajing, Tibet, China (YBJ), and the Institute of Atmospheric Physics (IAP), Beijing, China. The comparison shows that Himawari-8 missed more CTHs at night than FY-4A, especially at YBJ. It is found that the CTH difference (CTHD;radar CTH minus satellite CTH) for FY-4A and Himawari-8 is 0.06 ± 1.90 km and −0.02 ± 2.40 km at YBJ respectively, and that is 0.93 ± 2.24 km and 0.99 ± 2.37 km at IAP respectively. The discrepancy between the satellites and radar at IAP is larger than that at YBJ. Both satellites show better performance for mid-level and low-level clouds than for high-level clouds at the two sites. The retrievals from FY-4A agree well with those from Himawari-8, with a mean difference of 0.08 km at YBJ and 0.06 km at IAP. It is found that the CTHD decreases as the cloud depth increases at both sites. However, the CTHD has no obvious dependence on cloud layers and fractions. Investigations show that aerosol concentration has little impact on the CTHD. For high and thin clouds, the CTHD increases gradually with the increase of the surface temperature, which might be a key factor causing the regional discrepancy between IAP and YBJ.

Non-intrusive soil carbon content quantification methods using machine learning algorithms:A comparison of microwave and millimeter wave radar sensors

Agricultural and forestry biomass can be converted to biochar through pyrolysis gasification,making it a significant carbon source for soil.Applying biochar to soil is a carbon-negative process that helps combat climate change,sustain soil biodiversity,and regulate water cycling.However,quantifying soil carbon content conventionally is time-consuming,labor-intensive,imprecise,and expensive,making it difficult to accurately measure in-field soil carbon’s effect on storage water and nutrients.To address this challenge,this paper for the first time,reports on extensive lab tests demonstrating non-intrusive methods for sensing soil carbon and related smart biochar applications,such as differentiating between biochar types from various biomass feedstock species,monitoring soil moisture,and biochar water retention capacity using portable microwave and millimeter wave sensors,and machine learning.These methods can be scaled up by deploying the sensor in-field on a mobility platform,either ground or aerial.The paper provides details on the materials,methods,machine learning workflow,and results of our investigations.The significance of this work lays the foundation for assessing carbon-negative technology applications,such as soil carbon content accounting.We validated our quantification method using supervised machine learning algorithms by collecting real soil mixed with known biochar contents in the field.The results show that the millimeter wave sensor achieves high sensing accuracy(up to 100%)with proper classifiers selected and outperforms the microwave sensor by approximately 10%–15%accuracy in sensing soil carbon content.

A Method for Improving Ranging Accuracy of Short-range Millimeter Wave Radar

The range-velocity ambiguity caused by moving target influences on the ranging accuracy of a short-range millimeter wave radar greatly.A new method was presented in this paper to reduce the range-velocity ambiguity and improve the ranging accuracy by estimating parameters of the echo signal with fractional Fourier transform and self-correlation.And,a new quick searching algorithm was given also to increase the calculation speed.Compared to the Chinese remainder theorem method,the proposed method is excellent for its simplicity and reducing the computation complexity.The simulation results show its validity.

Compensation for the Influence of Radar-Target Relative Speed on Step Frequency MMW Target Identification

Aim To study the influence of radar-target relative speed on frequency MMW high-resolution ore-dimension distance profile and the compensation for it. Methods Based on the distance travelled by the electromagnetic wave, analyses were made for the compensation algorithm and the expression of the inverse FFT base distance was given.The relative importance of different compensation terms was studied in great detail. The concept of searching compensation was put forward. Results and Condclusion Dcm-△Dvimis the be distance of inverse FFT transformation, the effect caused by the distance △Dim on one-dimension profile is negligible, and the effect caused by the distance Dvim should not be neglected and must be compensated.

Relationship between Cloud Characteristics and Radar Reflectivity Based on Aircraft and Cloud Radar Co-observations

Cloud properties were investigated based on aircraft and cloud radar co-observation conducted at Yitong, Jilin, Northeast China. The aircraft provided in situ measurements of cloud droplet size distribution, while the millimeter-wavelength cloud radar vertically scanned the same cloud that the aircraft penetrated. The reflectivity factor calculated from aircraft measurements was compared in detail with sinmltaneous radar observations. The results showed that the two reflectivities were comparable in warm clouds, but in ice cloud there were more differences, which were probably associated with the occurrence of liquid water. The acceptable agreement between reflectivities obtained in water cloud confirmed that it is feasible to derive cloud properties by using aircraft data, and hence for cloud radar to remotely sense cloud properties. Based on the dataset collected in warm clouds, the threshold of reflectivity to diagnose drizzle and cloud particles was studied by analyses of the probability distribution function of reflectivity from cloud particles and drizzle drops. The relationship between refiectivity factor （Z） and cloud liquid water content （LWC） was also derived from data on both cloud particles and drizzle. In comparison with cloud droplets, the relationship for drizzle was blurred by many scatter points and thus was less evident. However, these scatters could be partly removed by filtering out the drop size distribution with a large ratio of reflectivity and large extinction coefficient but small effective radius. Empirical relationships of Z-LWC for both cloud particles and drizzle could then be derived.

A Ku and Ka Dual Band Experimental Radar

Due to the good performance of tracking low elevation target as compared to microwave and the superiority in penetrating smoke, dust, fog, and dry snow as compared to infrared, a Ku and Ka dual band experimental radar was designed and developed. This Ku and Ka dual band experimental radar is an arnplitute-comparison monopulse tracking and guiding radar. The constitution and parameters of this radar is described in paragraph 2. Paragraph 3 deals with two experiments for testing the tracking performances against low elevation target, and gives the important results. Both Ku and Ka band have high tracking precision when they track high elevation targets, while Ka band has much better tracking performance than Ku band when they track low elevation targets. Ka band can track a helicopter, whose radar cross section is about 6 square meters, at 40m, 20m, 10m, and even 5m above sea. Ku band can only track the same helicopter at 160m and higher above sea.

Convolution neural network and 77 GHz millimeter wave radar based intelligent liquid classification system

An intelligent liquid classification system based on 77 GHz millimeter wave radar and convolution neural network are proposed in this paper.The data are collected by the AWR1843 radar platform and processed by the neural network on the host PC in real-time.The doppler heatmap generated by radar signal processing is tried for the first time as the input of the system.The information carried by the heatmap in 2 dimensions is analyzed and the reason why the doppler heatmap could be used for classification is explained.The feasible experiment proved that the proposed method can successfully classify 8 kinds of common liquid with high accuracy.The result of the experiment is explained and the limitations of the experiment are discussed.It can be drawn that the combination of FMCW millimeter wave radar and convolution neural network is a method with great potential for liquid classification.The advantages of real time,non-invasive and unilateral measurement can also be used for the detection of dangerous liquids.

Study of Aircraft Icing Warning Algorithm Based on Millimeter Wave Radar

In order to avoid accidents due to aircraft icing, an algorithm for identifying supercooled water was studied. Specifically, a threshold method based on millimeter wave radar, lidar, and radiosonde was used to retrieve the coverage area of supercooled water and a fuzzy logic algorithm was used to classify the observed meteorological targets. The macrophysical characteristics of supercooled water could be accurately identified by combing the threshold method with the fuzzy logic algorithm. In order to acquire microphysical characteristics of supercooled water in a mixed phase, the unimodal spectral distribution of supercooled water was extracted from a bimodal or trimodal spectral distribution of a mixed phase cloud, which was then used to retrieve the effective radius and liquid water content of supercooled water by using an empirical formula. These retrieved macro- and micro-physical characteristics of supercooled water can be used to guide aircrafts during takeoff, flight, and landing to avoid dangerous areas.