Blind Zone Research Articles

Determining the characteristics of contact interaction between the two-row windshield wiper and a curvilinear glass surface

Among the evaluation criteria for determining the efficiency of vehicle windshield cleaning, the pressure distribution of the wiper rubber brush on the glass surface is important. The problem is the lack of this indicator standardization by the United Nations Economic Commission for Europe Rules (UNECE) regarding the windshield wipers certification. The inhomogeneity of the pressure distribution of the conventional wiper (the object of research) is additionally due to the mobility of the links of its mechanism and the plasticity of the rubber brush together with the blade. The pressure value should not be more than 30–50 kPa (33.4 kPa was recorded for the immobilized wiper case), and the external normal load on the frame should be kept within 20...30 N. Under a load of 24 N, the wiper blade was deformed by 1.48 mm (according to R43, it cannot exceed 1.5 mm). Further loading of the wiper frame causes two types of plastic deformation: local and global (loss of the rubber brush shape). Local displacements have increased to 1.82 mm, and the shape of the blade has acquired a "sliced" character, which causes thin jets of dirt. Global ones led to the rubber brush bending with the gap between it and the glass (5.7 mm) and caused the blind zone appearance. Models to mathematically predict layer-by-layer deformations of a conventional wiper were investigated. A model of a double-row blade with separate brushes was built, which enables the parallelization of water flows and explains the increase in the efficiency of its design. Hydrodynamic tests showed 1.58 times greater effectiveness compared to a classic single-row blade: the water flow rate was 15.61 vs 9.86 m/s. This technological advancement is the subject of a patent and a possible working prototype

Interpolation linearization predistortion technology for FMCW LiDAR.

Frequency-modulated continuous-wave (FMCW) laser ranging technology is an important development direction of light detection and ranging (LiDAR) for the future. It has the advantages of high ranging accuracy, high resolution, wide range, and no ranging blind zone. A distributed feedback laser can be used as a high-quality light source in FMCW laser ranging systems because of its wide frequency modulation range, simple frequency modulation mode, and small package. Aiming at the nonlinear problem of the laser in the frequency modulation process, we present a novel, to our knowledge, predistortion algorithm based on interpolation linear fitting to enhance the linearity of the FMCW laser for LiDAR systems. The sweeping frequency curve of the laser is obtained using the Hilbert transform, and then the sweeping frequency curve is segmented and linearly fitted to calculate the interpolated driving current signals corresponding to linear frequency changes. Using this method, we achieved a nonlinearity error lower than 1e-7 for the swept-frequency signal and demonstrated that the ranging error is less than ±5c m at a distance of 100m in the FMCW system. In addition, we also demonstrated a 3D static object point cloud with high imaging quality. Compared with the iterative predistortion algorithm based on the function fitting, this method avoids fitting errors at the inflection points of the triangular swept-frequency signal and the complexity of multiple iterative calculations. It enables rapid generation of pre-distorted swept-frequency signals, making it particularly suitable for real-time applications of automotive LiDAR systems.

The applications of sonar and radar systems in underwater detection

The development of sonar and radar systems utilizing sound and radio waves, respectively, has evolved significantly over the past two centuries. Recent decades have witnessed remarkable progress in remote sensing using high-frequency Doppler radars for marine surface parameter analysis. Understanding animal behavior around sustainable ocean energy sources is crucial for mitigating potential risks linked to such installations. Increasing the Signal-to-Noise Ratio (SNR) enhances signal reception. A technique estimating noise in the detection coil's first half determines the transfer function between the reference and detection coils. This enables separation of the Ultra-Nuclear Magnetic Resonance signal from noise, preserving it using non-linear fitting. Sonar systems, using sound waves, detect underwater objects, map surfaces, and track marine life. Sound waves emitted by the transmitter are reflected back to calculate distances. Challenges in sonar systems include a blind zone caused by submerged transducers in shallow water. To improve, SNR enhancement is critical. Sonar's future may rely on AI-assisted signal processing. Radar systems, operating with radio waves, share features with sonar, focusing on object detection and ranging. Improvement avenues include 3D imaging and AI-enhanced signal processing. In conclusion, sonar and radar systems are crucial tools for underwater exploration and object detection. While challenges persist, innovative solutions and AI advancements promise enhanced capabilities for both systems.

Understanding the Roles of Aerosols and Clouds in Environment, Meteorology and Climate with Advanced Lidar Remote Sensing Techniques

This Special Issue lists nine publications, covering the topics of advanced atmospheric lidar techniques, lidar retrievals, and lidar applications. The investigations listed here are diverse, but they are all focused on atmospheric lidars. Some urgent issues, for instance low blind zone detection and polarization detection at a near-infrared wavelength band, were discussed and explored. The results are helpful for extending atmospheric lidar applications. In terms of lidar retrievals, a planetary boundary layer height retrieval and an automatic lidar retrieval for aerosol optical properties were investigated in some of the publications, which can strengthen the atmospheric lidar capabilities. For lidar applications, a detailed analysis of the evolution of stratospheric aerosol and dust–cloud interactions was presented. In this Editorial, the articles published within this Special Issue are reviewed to highlight their innovative contributions and main research findings.

Analysis of a nuclear power plant’s responses to the open-phase fault of preferred power supply

This paper explores the phenomenon of open-phase fault of nuclear power plants for preferred power supply, focusing on three distinct scenarios, normal operation, start, and overhaul. For each scenario, the paper discusses the responses of existing relay protection devices, which are crucial for detecting and mitigating open-phase faults. These responses vary depending on the impedance at the fault location and the load rate of the plant. This paper highlights that open-phase faults can lead to three-phase imbalances in the power system, but their impact on voltage levels is not always straightforward due to the transformer and motor compensation effects. Additionally, the paper identifies blind zones in open-phase fault detection, where existing relays may fail to respond effectively, leading the plant to enter a station blackout state. In conclusion, the research underscores the importance of open-phase fault detection and protection in nuclear power plants. It reveals potential vulnerabilities in existing relay configurations and emphasizes the need for enhanced monitoring and safety measures to prevent extended plant operation under open-phase fault conditions.

Design and optimization of phase frequency detector through Taguchi and ANOVA statistical techniques for fast settling low power frequency synthesizer

In this work, a novel phase frequency detector (PFD) architecture using pass transistor logic is proposed. The circuit does not have a reset path, resulting in the elimination of blind zone and dead zone. The ϕ-V characteristics of the PFD were found to have better linearity across the range of −π to π due to the absence of blind and dead zones. The Taguchi and ANOVA statistical techniques were used to optimize the PFD. The optimized PFD exhibited a phase noise of −142.24 dBc/Hz, consumed 5.64 μW of power and had a maximum operating frequency of 5.25 GHz, and a delay of 10.65 ps. Using this PFD, a GHz-range synthesizer was designed, and its performance characteristics were obtained from circuit simulations using CADENCE Virtuoso. The synthesizer had a power consumption of 4.25 mW at a supply of 1.8 V, achieved a lock time of 2.95μs, and could generate frequencies ranging from 0.1 GHz to 4.75 GHz while occupying a chip area of 0.013 mm2. Moreover, the work introduced a new figure of merit, FoM. The synthesizer has potential applications in various devices such as radio receivers, televisions, mobile phones, satellite receivers, and GPS systems.

Low-level Arctic clouds: a blind zone in our knowledge of the radiation budget

Abstract. Quantifying the role of clouds in the earth's radiation budget is essential for improving our understanding of the drivers and feedback mechanisms of climate change. This holds in particular for the Arctic, the region currently undergoing the most rapid changes. This region, however, also poses significant challenges to remote-sensing retrievals of clouds and radiative fluxes, introducing large uncertainties in current climate data records. In particular, low-level stratiform clouds are common in the Arctic but are, due to their low altitude, challenging to observe and characterize with remote-sensing techniques. The availability of reliable ground-based observations as reference is thus of high importance. In the present study, radiative transfer simulations using state-of-the-art ground-based remote sensing of clouds are contrasted with surface radiative flux measurements to assess their ability to constrain the cloud radiative effect. Cloud radar, lidar, and microwave radiometer observations from the PS106 cruise in the Arctic marginal sea ice zone in summer 2017 were used to derive cloud micro- and macrophysical properties by means of the instrument synergy approach of Cloudnet. Closure of surface radiative fluxes can only be achieved by a realistic representation of the low-level liquid-containing clouds in the radiative transfer simulations. The original, most likely erroneous, representation of these low-level clouds in the radiative transfer simulations led to errors in the cloud radiative effect of 54 W m−2. In total, the proposed method could be applied to 11 % of the observations. For the data, where the proposed method was utilized, the average relative error decreased from 109 % to 37 % for the simulated solar and from 18 % to 2.5 % for the simulated terrestrial downward radiative fluxes at the surface. The present study highlights the importance of jointly improving retrievals for low-level liquid-containing clouds which are frequently encountered in the high Arctic, together with observational capabilities both in terms of cloud remote sensing and radiative flux observations. Concrete suggestions for achieving these goals are provided.

All-Day Object Detection and Recognition for Blind Zones of Vehicles Using Deep Learning

The neglect of perception ability to the surrounding traffic conditions has always been the major cause of traffic accidents and the inattention to blind spots is the most important factor during driving. Existing solutions are facing the problems of using expensive equipment, wrong classification of the target object type, not suitable for nighttime, and incorrectly determining if the target object is in the blind zones. This paper aims to improve driving perception ability by developing an all-day object detection and recognition system with more accurate performance for blind zones. The proposed method uses a general-purpose camera as a single input and a two-stage deep network architecture for object detection and recognition. The proposed system is based on a two-stage cascaded network structure. At first, the style conversion process is performed to convert the daytime and nighttime images with different brightness into consistent brightness. Then the objects in the visual blind zones are detected and identified. Therefore, the accuracy of object detection can be significantly improved. Due to the diversity and complexity of Taiwan’s road conditions, the public databases cannot effectively fulfill local application model training needs. Therefore, we have built training data set from available night images. Experimental results show that the proposed method has demonstrated promising performance in all-day object detection and recognition for blind zones.

Safety Monitoring Scheme of Gas Pipeline Network Based on Multi-homing Technology

The gas pipeline network is one of the key infrastructures for the energy supply of modern cities, and its safety is crucial to the life and development of urban residents. The traditional gas pipeline network monitoring method mainly relies on regular inspection and manual intervention, which has problems such as monitoring blind zone, slow response time and low monitoring accuracy. To address these issues, we introduce multi-homing technology into our gas network safety monitoring solution. First of all, we deploy multiple sensor nodes in key locations such as gas pipelines, valves, and connection points in the gas pipeline network, and each sensor node is equipped with multiple communication interfaces, which can be connected to different networks at the same time. We classify the collected data according to QoS traffic and assign links according to service level, and use multi-homing technology to perform multiple transmission and multiple reception, optimizing data transmission time and reliability.

Modeling spatiotemporal heterogeneity in interval-censored traffic incident time to normal flow by leveraging crowdsourced data: A geographically and temporally weighted proportional hazard analysis

Non-recurrent traffic congestion arising from traffic incidents is unpredictable but should be addressed efficiently to mitigate its adverse impacts on safety and travel time reliability. Numerous studies have been conducted about incident clearance time, while the recovery time, due to the limitations of data collection, is often inadvertently neglected in assessing incident-induced duration (i.e., the time from incident occurrence to the normal flow of traffic). Overlooking the recovery time is likely to underestimate the total incident-induced impact. Furthermore, the spatiotemporal heterogeneity of observed factors is not adequately captured in incident duration models. To address these gaps, this study specifically investigated traffic crashes as they reflect safety issues and are the primary cause of non-recurrent congestion. The emerging crowdsourced traffic reports were harnessed to estimate crash recovery time, which can complement the blind zone of fixed detectors. A geographically and temporally weighted proportional hazard (GWTPH) model was developed to untangle factors associated with the interval-censored crash duration. The results show that the GWTPH model outperforms the global model in goodness-of-fit. Many factors present a spatiotemporally heterogeneous effect. For example, the global model merely revealed that deploying dynamic message signs (DMS) shortened the crash time to normal. Notably, the GWTPH model highlights an average reduction of 32.8% with a standard deviation of 31% in time to normal. The study's findings and application of new spatiotemporal techniques are valuable for practitioners to localize strategies for incident management. For instance, deploying DMS can be very helpful in corridors when incidents happen, especially during peak hours.

Dual-Branch TransV-Net for 3-D Echocardiography Segmentation

Segmentation of left and right ventricles from 3D echocardiographic images is premised and key for quantitative analysis of cardiac function, which is important for pediatric cardiac diagnosis. Compared with 2D echocardiography, 3D echocardiography can fully represent ventricular structure without geometric inference. However, it usually takes experts several hours to obtain a 3D segmentation mask of the left and right ventricles. Therefore, a fast and automatic segmentation method is highly desired. Unfortunately, 3D echocardiography suffers from low contrast, unclear left and right ventricles borders, and blind zone. Existing segmentation methods usually have poor performance at ventricular boundaries. To deal with these problems, we propose a novel dual-branch TransV-Net (DBTV). DBTV comprises two parallel, interleaved, and relatively independent V-shaped encoder-decoder branches. The main branch acts on the original data to extract image features, and the auxiliary branch acts on edge maps to extract the additional edge features. To suppress noise and enhance the edge information, extra concatenations are added to bridge the features from the main and auxiliary branches. To reduce object missing caused by blind zone, a 3D transformer-based module is proposed in the bottom layer of the dual-branch structure to extract the global contexts. We do experiments on a self-collected dataset with 120 3D echocardiographic images from 60 cardiac sequences, and the dice score of 0.913 and 0.880 are obtained in the left and right ventricle segments, respectively. Each inference takes about two-thirds of a second for a single 3D frame.

P-Alert earthquake early warning system: case study of the 2022 Chishang earthquake at Taitung, Taiwan

A series of earthquakes that struck Taiwan's southern Longitudinal Valley on September 17 and 18, 2022 severely damaged several buildings in Taitung and Hualien. The Chishang earthquake, which had a magnitude of ML 6.8 and a large foreshock with a magnitude of ML 6.6 the day before, was the mainshock in this sequence. The strongest intensity reported in the epicentral region during this earthquake sequence, which was 6 + , is the highest ever recorded since the Central Weather Bureau (CWB, renamed as the Central Weather Administration since September 15, 2023) revised its seismic intensity scale. National Taiwan University (NTU) has operated a low-cost earthquake early warning (EEW) system known as the P-Alert for a decade. In this study, we demonstrate the performance of the P-Alert network during the 2022 Chishang earthquake and the largest foreshock. The P-Alert network plotted shake maps during these earthquakes that displayed various values within 5 min. The high shaking areas on these maps were in good agreement with observed damages during this earthquake, providing valuable insights into rupture directivity, a crucial component of earthquake engineering. Individual P-Alert stations acted as on-site EEW systems and provided a lead time of 3–10 s within the blind zone of CWB. For the ML 6.8 mainshock, there was a lead time of at least 5 s, even up to 10 s, demonstrating their effectiveness in the blind zone. The P-Alert regional EEW system provided the first report about 9 s and 7 s after the mainshock and the largest foreshock occurrence, respectively, with estimated magnitudes of 5.74 and 5.67. The CWB system estimated magnitudes of 6.72 and 6.16 in the first report, respectively, about 7 s and 9 s after the earthquake occurrence. The timeliness of the two systems were not significantly different. Despite the effectiveness of the P-Alert network, data loss due to connection interruptions prompted us to develop a new compact data logger for improved data availability.

A digital Twin-based bi-directional deduction method for the full-pose of the Floating connection mechanism

Full-pose monitoring of the Floating Connection Mechanism (FCM) still exists blind zone, unreliable sensing data, etc., this paper proposes a digital twin bi-directional deduction method for the pose of starting/end point of the FCM. Firstly, considering the pose of the connection equipment, an optimization of the motion law of the FCM based on Conformal Geometry Algebra (CGA) is realized; secondly, the virtual application of optimization motion laws of the FCM is achieved by marking key points and extracting key information; then, referring to the information enhancement scheme obtained by adding digital twin infrared range sensors and a bi-directional deduction method is proposed for the full-pose of the FCM; finally, the experimental validation shows that the motion law of the FCM proposed in this paper is in line with the actual motion and generates better analytical results, the pose deduction accuracy is over 87%, providing new ideas for equipment pose monitoring.

A low-jitter and low-phase noise switched-loop filter PLL using fast phase-error correction and dual-edge phase comparison technique

This paper presents a low-jitter and low-phase noise type-II charge pump and switched-loop filter-based (SLF) phase-locked loop (PLL) with a fast phase error correction technique (FPEC) and a newly proposed dual-edge phase comparator (DEPC). To improve the phase noise, the bandwidth of the proposed PLL is extended to fREF/5 using the proposed DEPC. It consists of four D flip-flops in true single-phase clock (TSPC) logic, four current-mode logic-based inverters, and two logic OR gates. Unlike conventional DEPCs, the proposed DEPC has a small reset pulse width duration and negligible blind zone, and due to this, it can perform at higher frequencies along with an increased phase detection range. The proposed architecture is implemented for a 2.4 GHz output frequency that embodies DEPC and SLF using the FPEC technique in an 180-nm Semi-Conductor Laboratory (SCL) CMOS process. The proposed DEPC can work up to a maximum of 3.45 GHz frequency with 145 ps of reset delay. The proposed PLL accomplishes the 40 MHz bandwidth with a 200 MHz reference clock. The simulated rms jitter integrated from 10 kHz to 100 MHz and the phase noise of the output signal at an offset of 1 MHz are 860 fs and −122 dBc/Hz respectively. The proposed architecture occupies an area of 0.032 mm2 and consumes 8.2 mW from 1.8 V power supply.

A Through-Transmission Ultrasonic Method for the Detection of Ferrite Tile Defects

A through-transmission ultrasonic method is proposed to address limitations in conventional ultrasonic reflection methods for non-destructive testing of ferrite tiles. The method utilizes a dual-probe configuration on both sides of the test piece to measure ultrasonic transmission signals, overcoming issues related to blind zones and orientation limitations in pulse-echo reflection methods. This method demonstrates excellent capabilities for full inspection of internal and external defects in ferrite tiles. Physical field finite element simulations were conducted to analyze detection capabilities and a transmissive testing system is developed based on the simulation results. Experimental validation was performed on artificially manufactured quantified defect samples in aluminum alloy, and the same testing system was applied to evaluate ferrite tile samples. The results confirmed the effectiveness of the system in distinguishing defective (NG) signals from normal defect-free (OK) signals, with a recall rate of at least 95% on samples of various sizes up to 0.1 mm. This research provides insights for quality control and defect detection technology in ferrite tiles.

Narrowing the Blind Zone of the GPM Dual-Frequency Precipitation Radar to Improve Shallow Precipitation Detection in Mountainous Areas

Abstract The Dual-Frequency Precipitation Radar (DPR), which consists of a Ku-band precipitation radar (KuPR) and a Ka-band precipitation radar (KaPR) on board the GPM Core Observatory, cannot observe precipitation at low altitudes near the ground contaminated by surface clutter. This near-surface region is called the blind zone. DPR estimates the clutter-free bottom (CFB), which is the lowest altitude not included in the blind zone, and estimates precipitation at altitudes higher than the CFB. High CFBs, which are common over mountainous areas, represent obstacles to detection of shallow precipitation and estimation of low-level enhanced precipitation. We compared KuPR data with rain gauge data from Da-Tun Mountain of northern Taiwan acquired from March 2014 to February 2020. A total of 12 cases were identified in which the KuPR missed some rainfall with intensity of >10 mm h−1 that was observed by rain gauges. Comparison of KuPR profile and ground-based radar profile revealed that shallow precipitation in the KuPR blind zone was missed because the CFB was estimated to be higher than the lower bound of the range free from surface echoes. In the original operational algorithm, CFB was estimated using only the received power data of the KuPR. In this study, the CFB was identified by the sharp increase in the difference between the received powers of the KuPR and the KaPR at altitude affected by surface clutter. By lowering the CFB, the KuPR succeeded in detection and estimation of shallow precipitation. Significance Statement The Dual-Frequency Precipitation Radar (DPR) on board the GPM Core Observatory cannot capture precipitation in the low-altitude region near the ground contaminated by surface clutter. This region is called the blind zone. The DPR estimates the clutter-free bottom (CFB), which is the lower bound of the range free from surface echoes, and uses data higher than CFB. DPR consists of a Ku-band precipitation radar (KuPR) and a Ka-band precipitation radar (KaPR). KuPR missed some shallow precipitation more than 10 mm h−1 in the blind zone over Da-Tun Mountain of northern Taiwan because of misjudged CFB estimation. Using both the KuPR and the KaPR, we improved the CFB estimation algorithm, which lowered the CFB, narrowed the blind zone, and improved the capability to detect shallow precipitation.

A Three-element Risley Prism Continuous Scanning System Based on Linear Constraint

In the three-element Risley prism system, non-linear errors, the infinite solution, the blind zone and the singularity are primary factors that affect the scanning accuracy of the system. In this paper, an accurate analytical model of the three-element Risley prism system is proposed. To reduce the errors of single-wedge nonlinear constraint equation, a dimensionality reduction method constrained by a single wedge is adopted. In addition, a reliable accuracy of 5mm is employed to achieve a full-area scanning of the system, and a single wedge linear constraint equation with an angular increment is established to control singularities. Simulation results show that the wedge rotation angle curve is stable and continuous in the process of inverse solution, and the full coverage of the scanning area can be achieved by assigning scanning accuracy to 5mm, which indicates that the proposed method can effectively solve the problems and achieve full-area scanning in the system.

Low Blind Zone Atmospheric Lidar Based on Fiber Bundle Receiving

Atmospheric constituents feature a large vertical gradient in concentration, especially at the first few hundred meters over the earth’s surface. Atmospheric lidar usually cannot cover this range due to the incomplete overlap effect or the limited dynamic range of detectors. This drawback is well known as the blind zone effect, which hinders the application of atmospheric lidars in many aspects. In this work, a method based on an optical fiber bundle was proposed to mitigate the blind zone effect. An optical fiber head with several stages, installed at the focal plane of the telescope, is used to receive backscatter light from different range levels. The design of the optical fiber head is analyzed with the ray-tracing technique. The optical fiber installed at the highest stage of the fiber head can collect far-range light like a small aperture, and all the other optical fibers are bundled into a near-range detection channel to receive backscatter light from the first few hundred meters. This special design can avoid the near-range light loss in conventional lidar systems, usually equipped with a small aperture. Different optical attenuations are then applied to near-range and far-range channels to suppress the overall signal dynamic range. This light-receiving method was applied in a 1030 nm elastic lidar, in which a fiber bundle with a three-stage fiber head was fabricated and installed. A test experiment was performed to verify this approach. A good agreement between simulations and in-system results was found. Based on this design, the blind zone of the lidar system is less than 50 m, and the detectable range can be over 10 km along the lidar’s line of sight with a single telescope receiver. This approach brings a new way of designing atmospheric lidar with a low blind zone and can strengthen our ability to monitor urban pollution and promote land-atmosphere interaction research.

Radar‐Derived Snowfall Microphysical Properties at Davis, Antarctica

AbstractAntarctic precipitation remains poorly characterized and understood, especially within the boundary layer. This is due in part to a still‐limited amount of surface‐based remote sensing observations. A suite of cloud and precipitation remote‐sensing instruments including a W‐band cloud radar and a K‐band Micro Rain Radar (MRR) were used to characterize snowfall over Davis (69°S, 78°E). Surface snowfall events occurred when boundary layer wind speeds were weaker, temperatures were warmer, and relative humidity over ice higher than when virga were present. The presence of virga is associated with Föhn winds due to the location of Davis in the lee of an ice ridgeline. Dual wavelength ratio values from the summer indicate particle aggregation at temperatures of −14° to −10°C, consistent with observations made elsewhere, including in the Arctic. Riming frequency increases for temperatures above −10°C and reaches 6.5% at −3°C. No temperature dependence of rime mass fraction was found. Sublimation of snowfall mass aloft was 50% between the snow peak at 1.2 km and 205 m altitude, which occurs within CloudSat’s “blind zone.” Given the common prevailing wind direction and numerous ice ridgelines along much of the East Antarctic coastline, these Davis results can be used as a basis to further understand snowfall across the Antarctic region.

Assessing Arctic low-level clouds and precipitation from above – a radar perspective

Abstract. Most Arctic clouds occur below 2 km altitude, as revealed by CloudSat satellite observations. However, recent studies suggest that the relatively coarse spatial resolution, low sensitivity, and blind zone of the radar installed on CloudSat may not enable it to comprehensively document low-level clouds. We investigate the impact of these limitations on the Arctic low-level cloud fraction, which is the number of cloudy points with respect to all points as a function of height, derived from CloudSat radar observations. For this purpose, we leverage highly resolved vertical profiles of low-level cloud fraction derived from down-looking Microwave Radar/radiometer for Arctic Clouds (MiRAC) radar reflectivity measurements. MiRAC was operated during four aircraft campaigns that took place in the vicinity of Svalbard during different times of the year, covering more than 25 000 km. This allows us to study the dependence of CloudSat limitations on different synoptic and surface conditions. A forward simulator converts MiRAC measurements to synthetic CloudSat radar reflectivities. These forward simulations are compared with the original CloudSat observations for four satellite underflights to prove the suitability of our forward-simulation approach. Above CloudSat's blind zone of 1 km and below 2.5 km, the forward simulations reveal that CloudSat would overestimate the MiRAC cloud fraction over all campaigns by about 6 percentage points (pp) due to its horizontal resolution and by 12 pp due to its range resolution and underestimate it by 10 pp due to its sensitivity. Especially during cold-air outbreaks over open water, high-reflectivity clouds appear below 1.5 km, which are stretched by CloudSat's pulse length causing the forward-simulated cloud fraction to be 16 pp higher than that observed by MiRAC. The pulse length merges multilayer clouds, whereas thin low-reflectivity clouds remain undetected. Consequently, 48 % of clouds observed by MiRAC belong to multilayer clouds, which reduces by a factor of 4 for the forward-simulated CloudSat counterpart. Despite the overestimation between 1 and 2.5 km, the overall low-level cloud fraction is strongly reduced due to CloudSat's blind zone that misses a cloud fraction of 32 % and half of the total (mainly light) precipitation amount.