A Low-Cost Portable SDIMM for Daytime Atmospheric Optical Turbulence Measurement in Observatory Site Testing: Primary Results from Ali Site

Abstract

Atmospheric optical turbulence intensity, quantified by the Fried parameter ($r_0$), serves as a critical metric for astronomical site testing and selection. The Solar Differential Image Motion Monitor (SDIMM), adapted from the methodology of the Differential Image Motion Monitor (DIMM), is a dedicated instrument for daytime $r_0$ measurements. Conventional SDIMM systems typically employ telescopes with apertures ≥30 cm and reconstruct wavefront segmentation at the exit pupil, resulting in bulky configurations that impede portability. To address the demands of multi-site surveys, we developed a low-cost, portable SDIMM system that directly adopts the DIMM optical path without backend wavefront reconstruction, instead deriving $r_0$ through image processing algorithms. Integrated with a 20 cm aperture telescope, the system achieves a total weight of <20 kg, significantly enhancing field portability. This paper details the instrument’s architecture, measurement principles, and comparative tests with a traditional SDIMM, demonstrating strong consistency between the two systems. Field measurements conducted at the Ali Observatory (elevation: 5050 m) from 16 August to 10 December 2024 yielded the site’s first continuous daytime $r_0$ dataset, with values ranging from 1.5 cm to 12 cm and a mean of 4.09 cm. The compact SDIMM provides a cost-effective and easily deployable solution for comparative daytime $r_0$ assessments across multiple candidate astronomical sites.

1. Introduction

Atmospheric optical turbulence degrades telescopic imaging quality through image motion, distortion, and scintillation, thereby imposing substantial constraints on ground-based astronomical observations [1,2]. For solar telescopes, daytime turbulence is particularly pronounced due to solar radiation-induced thermal disturbances, posing challenges for high-resolution imaging [3]. As a fundamental parameter quantifying atmospheric optical turbulence, the Fried parameter ($r_0$) [4] serves as a critical metric for astronomical site testing, telescope design, and adaptive optics optimization. In astronomical site testing campaigns, $r_0$ is prioritized as the primary observational parameter due to its direct correlation with seeing conditions [5]. Thus, accurately measuring $r_0$ constitutes a critical task for astronomical site testing.

Selecting optimal sites for astronomical observatories requires evaluating multiple environmental parameters, including seeing; atmospheric extinction; sky brightness; and weather conditions such as cloud cover, wind speed, humidity, and dust [6,7]. High-altitude locations are often preferred for solar observations due to reduced atmospheric turbulence and clearer skies, as demonstrated by studies at sites like Big Bear Solar Observatory (2067 m) and Fuxian Lake (1721 m) [8,9,10]. In regions like Tibet, high-altitude sites show promise for stable daytime observing conditions [11,12].

The differential image motion monitor (DIMM) [6,7,13] is the universally accepted instrument for measuring nighttime seeing at astronomical site testing. The nighttime $r_0$ is determined by measuring the variance of relative motion between two stellar images formed through separate sub-apertures, usually cut out in a single larger telescope pupil by a dual-aperture mask. This approach offers the key advantage of rendering measurements fundamentally immune to instrument shake and tracking errors through its differential design [14]. For daytime seeing measurements, the observational target transitions from point-source stellar images to the extended solar disk, introducing inherent complexities. Building on DIMM principles, Liu [9] developed a methodology utilizing a focal-plane slit and a downstream wavefront segmentation system to obtain two parallel slit images of the same solar limb in a conjugate image plane, thereby enabling daytime seeing measurements analogous to those of the DIMM. This instrument, termed the Solar Differential Image Motion Monitor (SDIMM), has become the preferred tool for solar telescope site testing [15,16].

Figure 1 illustrates the optical configuration of a traditional SDIMM, where kinematic information of the solar limb is extracted from east–west-oriented slits in the primary image plane. The wavefront is subsequently segmented by two sub-aperture holes positioned at the telescope’s exit pupil. A small wedge prism inserted into one of the segmented sub-wavefronts induces a fixed wavefront tilt perpendicular to the slit axis, generating two parallel slit images of the solar limb in the conjugate image plane [17]. To optimize the downstream wavefront segmentation system, traditional SDIMMs typically employ telescopes with apertures exceeding 30 cm and focal ratios greater than f/8 as primary mirrors. This design requirement results in significant instrument volume and length, limiting its suitability for mobile site testing applications.

Preliminary site-testing campaigns in western China have identified several high-altitude candidate sites, including Ali [18,19,20,21], Muztagh-ata [22,23], Lenghu [24], and Daocheng [25], all located at elevations exceeding 4000 m. To facilitate efficient comparative assessments of daytime seeing across these remote locations, we developed a low-cost, portable Solar Differential Image Motion Monitor (LP-SDIMM). Unlike traditional systems that rely on slit-based wavefront reconstruction, the LP-SDIMM directly adapts the DIMM optical configuration for solar limb observation. By computationally processing the solar edge features, the system derives differential image motion to quantify daytime seeing. The elimination of backend reconstruction optics enables the use of smaller-aperture telescopes, resulting in a compact system (<20 kg) with enhanced portability. Moreover, the expanded telescope field of view captures nearly half of the solar limb, allowing for triangulation-based measurements of daytime turbulence profiles ($C_n^2\left(h\right)$), which is analogous to the Profiler of the Differential Solar Limb (PDSL) technique [26].

In 2016, Ikhlef et al. introduced MISOLFA, designed to characterize the spatio-temporal features of daytime turbulence, emphasizing the importance of temporal resolution and marking a shift towards more dynamic assessments in solar site testing [27]. Despite this, limited longitudinal datasets at the time necessitated more extensive time-resolved observations for comprehensive site characterization. To address this gap, Song et al. developed the Profiler of the PDSL system, which captures solar limb brightness fluctuations to infer turbulence profiles. While effective, such systems often require complex optical setups, limiting portability and ease of deployment [17]. Building on this, the LP-SDIMM was developed, simplifying the optical design by eliminating wavefront reconstruction optics, providing a compact, cost-effective solution ideal for mobile deployment across multiple sites. The evolution of these measurement technologies has led to the widespread adoption of SDIMM variants, including LP-SDIMM, with recent studies showing they provide reliable $r_0$ estimates under diverse field conditions, underscoring their importance in long-term, multi-site solar seeing campaigns [16,28,29].

The purpose of this paper is to present the instrument’s architecture, measurement principles, and field validation results of the LP-SDIMM. Section 2 first outlines the theoretical framework for LP-SDIMM-based measurements of the daytime $r_0$ and the turbulence profiles ($C_n^2\left(h\right)$), followed by a description of the solution methodology. Section 3 detail the instrument implementation, encompassing hardware design, computational algorithms, and comparative tests with traditional SDIMM. Section 4 focuses on field measurements conducted at the Ali site from 16 August to 10 December 2024, presenting the first continuous daytime seeing dataset for this site. Section 5 provides a detailed analysis and discusses potential technical improvements, while Section 6 concisely summarizes the findings and potential applications.

2. Measurement Principles

Figure 2 illustrates the optical schematic of the LP-SDIMM. Our design retains the core DIMM optical path—employing a dual-aperture mask with a wedge prism—while incorporating a solar filter at the entrance pupil of each sub-aperture to enable direct solar observations [7]. Unlike differential point-source stellar images, the LP-SDIMM captures overlapping differential solar limb images, as demonstrated in the rightmost panel of Figure 2. Crucially, despite this overlap, the sharp intensity gradient at the solar limb permits the precise extraction of image motion data through edge detection algorithms. The wavefronts forming these dual limb images traverse distinct atmospheric paths, resulting in differential motion signatures. The statistical correlation of these motions directly relates to seeing conditions: higher correlation indicates superior atmospheric stability. Consequently, daytime $r_0$ values are inferred from wavefront angle-of-arrival fluctuations derived computationally from the moving solar limb features.

2.1. Measurement Principles of $r_0$

LP-SDIMM offers two methods for obtaining daytime $r_0$ parameters. The first is based on the measurement principle of DIMM. The second calculates $C_n^2\left(h\right)$ by treating two points on the solar limb as a double star (using different separation angles) and derives $r_0$ through the integration of $C_n^2\left(h\right)$. In most cases, we employ the DIMM-based measurement approach.

The variance of differential image motion is related to ($r_0$) through the following formula:

$${\delta }_d^2(d,D)=K{\lambda }^2{r}_0^{-{\displaystyle \frac{5}{3}}}{D}^{-{\displaystyle \frac{1}{3}}}$$

(1)

Here, $\lambda$, D, and d denote the wavelength, sub-aperture diameter, and separation between the two sub-apertures (as labeled D and d in Figure 2), respectively. Additionally, ${\delta }_d^2(d,D)$ denotes the variance of wavefront angle-of-arrival fluctuations at the solar limb (i.e., the directly measurable quantity from solar limb images), while K is a constant determined by the ratio $B(B=d/D)$ and the edge displacement direction (longitudinal $K_l$ or transverse $K_t$). The commonly used equation factors are given as follows [7]:

Vertical Motion Variance:

$$K_l=0.364\left[1-0.532B^{-{\displaystyle \frac{1}{3}}}-0.024B^{-{\displaystyle \frac{7}{3}}}\right]$$

(2)

Horizontal Motion Variance:

$$K_t=0.364\left[1-0.7982B^{-{\displaystyle \frac{1}{3}}}-0.018B^{-{\displaystyle \frac{7}{3}}}\right]$$

(3)

Using the aforementioned methodology, the parameter $r_0$ can be evaluated in both longitudinal and transverse directions. However, in LP-SDIMM, measurable parameters are restricted to the longitudinal component due to the constraint that image displacement occurs perpendicularly to the solar limb. The simplified formula is:

$$r_0^{-{\displaystyle \frac{3}{5}}}={\displaystyle \frac{K_l{\lambda }^2{D}^{-\frac{1}{3}}}{{\sigma }_d^2}}$$

(4)

The parameter $r_0$ can also be estimated from the integral of $C_n^2\left(h\right)$ already calculated:

$$r_0={\left[0.423{({\displaystyle \frac{2\pi }{\lambda }})}^{2}sec\left(z\right)\int C_n^2\left(h\right)dh\right]}^{-{\displaystyle \frac{3}{5}}}$$

(5)

This necessitates first retrieving the $C_n^2\left(h\right)$ profile before $r_0$ can be computed.

2.2. Estimating $C_n^2\left(h\right)$

The relationship between the covariance of wavefront angle-of-arrival fluctuations at different separation angles and $C_n^2\left(h\right)$ is expressed as follows:

$$C_{∆\phi }\left(\theta \right)=\int C_n^2\left(h\right)k_{\alpha }\left(d,h,\theta \right)dh$$

(6)

where h denotes the height of the turbulent layer; d refers to the center distance of the two sub-apertures; and $k_{\alpha }\left(d,h,\theta \right)$, alternatively termed the kernel function, represents a normalized covariance triplet. Discretizing and substituting Equation (6) yields:

$$C_{\phi }\left({\theta }_j\right)=\sum _{i=0}^{h_{max}}∆h_i{C}_n^2\left(h_i\right)k_{\alpha }\left(d,h_i,{\theta }_j\right)$$

(7)

The kernel function $k_{\alpha }\left(d,h_i,{\theta }_i\right)$ is derivable through turbulence modeling and hardware receiver parameters, ultimately constituting an $N\ast M$ matrix, where N denotes the number of layers in the reconstructed atmospheric grid, and M corresponds to the number of angular separations $\theta$ along the solar limb. $C_{\phi }\left({\theta }_j\right)$ is the measured covariance of wavefront angle-of-arrival fluctuations at different separation angles on the solar limb [5,27]. $C_n^2\left(h_i\right)∆h_i$ denotes a $1\ast N$ matrix of the $C_n^2\left(h_i\right)$. The $C_n^2\left(h\right)$ retrieval method is analogous to that of PDSL, with detailed solutions available in Song et al. [26]. Notably, while both methods utilize edge detection and computational image analysis for turbulence measurement, the PDSL system requires larger apertures and a more complex processing framework, whereas LP-SDIMM simplifies the optical path by eliminating wavefront reconstruction optics, enhancing portability and affordability.

3. Instrument Implementation

3.1. Hardware Configuration

The LP-SDIMM retains the core optical configuration of a traditional DIMM but adapts it for solar observations by integrating a solar filter directly at the telescope sub-aperture. This design choice eliminates the need for a dedicated wavefront reconstruction system, streamlining the optical path. The instrument parameters are as follows:

• Telescope: A Ritchey-Chretien(RC) system with a 200 mm aperture and an $f/8$ focal ratio, constructed with a carbon fiber tube.
• Aperture Mask: A dual sub-aperture entrance pupil mask was employed, featuring an effective sub-aperture diameter D=50 mm and a sub-aperture center distance of 150 mm.
• Wedge prism: Defined as $\alpha ={70}^{\prime }$, with an effective aperture diameter of $\mathrm{\Phi }=50$ mm.
• Detector: A Basler GS3-U3-60QS6M cameraThe GS3-U3-60QS6M camera (FLIR Systems, Inc., Wilsonville, OR, USA), procured from China, equipped with a pixel size of $4.54\mathrm{\mu m}\times 4.54\mathrm{\mu m}$ and a pixel count of $2048\times 2048$. Installed at the focal plane of the telescope, it can capture 45% of the solar limb at a frequency of 36 Hz. The camera is equipped with a custom image acquisition board that implements real-time subframe selection, reducing data throughput while maintaining high temporal resolution.
• Solar filter: An astroSolar Planetarium filter with a transmission rate of $1\times {10}^5$ was utilized.
• Mount: A lightweight German equatorial mount (e.g., Sky-Watcher EQ5 Pro (Sky-Watcher, Taoyuan, Taiwan) procured from China), with a payload capacity of 15 kg is employed to balance tracking precision and portability.

Compared to conventional SDIMM, the LP-SDIMM retains the fundamental methodology for daytime $r_0$ measurements through differential image motion analysis while achieving significant reductions in physical footprint and cost. As summarized in

Table 1. Comparative specifications of SDIMM systems.

Parameter	Conventional SDIMM	LP-SDIMM	Reduction
Aperture	30–40 cm	20 cm	30–50%
Total mass (w/mount)	50–70 kg	20 kg	75–80%
Hardware cost	¥100,000–200,000 RMB	<¥30,000 RMB	>70%
Optical length	1.6–2 m	0.8–1 m	>50%
Deployment time	>10 min	<5 min	50%

, the LP-SDIMM features a total deployment weight of approximately 20 kg (including mount) with complete hardware costs under ¥30,000 RMB. Figure 3 shows a physical comparison of the LP-SDIMM and traditional SDIMM.

3.2. Optical Calibration

Before data collection, the optical wedge must be precisely adjusted to ensure that the centers of the two solar limb images are aligned on the same horizontal plane during differential measurements. The specific steps are as follows:

1.

Point the telescope at a reference star. This is a critical step for conducting optical measurements.

2.

Fix the camera and rotate the optical wedge until the two star spots align parallel to the horizontal axis of the detector. During this process, the two separate solar images will also be aligned horizontally with each other.

3.3. Data Collection

To enhance the computational accuracy of real-time measurements, the phase consistency principle (which is a technique that evaluates the consistency of phase information across different spatial frequencies to achieve precise edge detections) [30] is employed to identify the solar limb and extract the corresponding edge points. These edge points are then fitted using the Least Squares Method [31]. From the periphery of the fitted solar limb, a $400\times 400$ pixel region is extracted to focus on the most relevant data. Subsequently, the variance of the horizontal displacement between the centers of the two circles is calculated to determine the value of $r_0$. The data collection procedure consists of the following steps: (for detailed algorithms, please refer to [26]):

1.

Obtain a full-size image of the Sun from the CCD image (Figure 4a).

2.

Detect and fit the centers of the two solar images using the Least Squares Method, based on edge detection points highlighted in pink and green in Figure 4b.

3.

Extract a $400\times 400$ pixel region around the solar edges.

4.

Acquire images from the extracted region with an exposure time of $10\mathrm{ms}$, and set sampling positions at $5\times 5$ pixels (marked with red squares in Figure 4c).

5.

Measure the distance between the two solar edges.

6.

Repeat steps 5 and 6 to collect 1000 image samples.

7.

Calculate the distance variance and value of $r_0$ using Formulas (3) and (5).

3.4. Calibration and Validation

The LP-SDIMM was calibrated against a reference SDIMM at the Yunnan Observatory over a 3-day campaign in March 2024, as shown in Figure 3. Comparative analysis showed excellent agreement between the two instruments, with a mean bias of only 0.08 cm in $r_0$ measurements and a root mean square error (RMSE) of less than 0.2 cm across the measured range of 1–15 cm.

To further validate the daytime measurements, we performed simultaneous observations with an equatorial mount, which yielded a strong Pearson correlation coefficient of 0.92. Statistical analysis of the comparative data revealed that the LP-SDIMM produced a mean $r_0$ of 2.91 cm (median = 2.9 cm), while the reference SDIMM showed a mean of 2.94 cm (median = 2.92 cm), demonstrating the reliability of our portable system.

Figure 5 presents representative results from a single day of comparative testing. The left panel shows the time series of $r_0$ values from both instruments, displaying close tracking throughout the day. The right panel provides a histogram comparison of the measurements, visually confirming the consistency between systems. Additional validation tests under various seeing conditions confirmed the LP-SDIMM’s robust performance across its operational range.

4. Primary Results at Ali Site

The field testing of the Low-cost Portable Solar Differential Image Motion Monitor (LP-SDIMM) was conducted at the Ali site in Tibet, at an altitude of 5050 m, from 16 August to 10 December 2024. This section details the installation process, data acquisition methodology, and preliminary results, providing the first continuous daytime seeing dataset for this high-altitude site. The following subsections outline the procedures and findings, demonstrating the LP-SDIMM’s effectiveness in measuring daytime atmospheric optical turbulence.

4.1. Detailed Installation Process

The installation of the LP-SDIMM at the Ali site was executed with meticulous attention to environmental conditions and operational demands. The compact system was mounted on a stable tripod positioned on a concrete platform to minimize ground-induced vibrations (Figure 6). Coarse alignment was initially achieved using a magnetic compass, aligning the telescope mount with the local meridian while accounting for the site’s magnetic declination. For precise polar alignment, the North Star (Polaris) was centered in the telescope’s field of view using a polar scope with a reticle, aligning the mount’s polar axis within 0.1 degrees of the celestial pole. A three-star calibration procedure, using bright stars such as Vega, Deneb, and Altair, was then carried out to correct for any residual misalignment in the equatorial mount’s tracking system, achieving tracking accuracy better than 2 arcseconds. During daytime operations, solar ephemeris data was used to program the telescope for precise tracking of the Sun. Fine focus adjustments were made by observing sunspots with a high-resolution eyepiece, ensuring optimal sharpness for accurate turbulence measurements.

Environmental conditions, such as wind speeds of up to 8 m/s and temperatures ranging from −5 °C to 15 °C, were continuously monitored to ensure stability during setup. This careful installation process ensured that the LP-SDIMM could reliably capture high-precision daytime turbulence data under the challenging conditions of a high-altitude, remote observatory.

4.2. Data Acquisition Process

Data acquisition was performed remotely using a ruggedized control system that integrated a custom software suite. The system enabled the efficient management of the telescope’s solar tracking and the LP-SDIMM’s imaging module. The camera (2048 × 2048 pixels) continuously captured solar limb images at 36 Hz, synchronized with the telescope’s tracking system to ensure precise alignment within 1 arcsecond.

To enhance temporal resolution, the images were cropped to a 400 × 400 pixel region around the solar limb, increasing the sampling rate to 120 Hz, which was adequate for capturing the rapid dynamics of atmospheric turbulence. The system was remotely controlled, with real-time image quality monitoring enabling adjustments to exposure settings to account for fluctuations in solar brightness. Automated quality control algorithms identified and flagged images with excessive noise or cloud interference, ensuring that only high-quality data were processed.

All acquired images were stored on a local solid-state drive and periodically uploaded to a secure cloud server for further offline analysis. To mitigate the risk of data loss due to the challenging environmental conditions, daily backups were performed. The system maintained an uptime of 90% throughout the testing period, from 16 August to 10 December 2024, despite occasional disruptions from intermittent network connectivity and dust storms.

4.3. Field Testing Results and Analysis

The LP-SDIMM provided continuous daytime ($r_0$) measurements with a median of 3.87 cm (mean: 4.09 cm; IQR: 3.32–4.56 cm), indicating moderate variability driven by solar heating (Figure 7). Data coverage was balanced between morning (60.83%, 09:00–14:00) and afternoon (39.17%, 14:00–19:00), with 95% of values falling within 2.8–5.1 cm. The instrument’s precision (SD = 0.2 cm) matches commercial systems at lower cost.

Figure 8 illustrates the monthly variation of daytime Fried parameter ($r_0$) values observed at Ali Observatory from August to December. The data reveal a distinct diurnal trend, with $r_0$ values generally decreasing throughout the day due to increased thermal activity. The median $r_0$ values indicate moderate average seeing conditions. Notably, August recorded the lowest median $r_0$ value (3.30 cm), suggesting comparatively poorer seeing conditions during that month. September, November, and December display similar $r_0$ statistics, suggesting relatively stable atmospheric conditions. October exhibits a slightly lower mean $r_0$ value compared to the other months.

Figure 9 illustrates the probability density distribution of the $r_0$ value, smoothed using the kernel density estimation (KDE) method. The x-axis represents value of $r_0$, while the y-axis denotes the relative probability density of the corresponding values. The majority of the $r_0$ value is concentrated within the 1.5–12 cm range, indicating generally stable observation conditions at this site. The distribution curve exhibits a short right tail, whereas the extended tail in the lower value of $r_0$ area may be attributed to extreme turbulence.

Figure 10 illustrates the daily median variation in $r_0$ throughout the observation period. The x-axis denotes the dates, while the y-axis represents the median value of $r_0$. Overall, the median curve exhibits slight fluctuations, with a mean value of $3.69\mathrm{cm}$ and a median of $3.47\mathrm{cm}$, indicating that daytime atmospheric conditions remained relatively stable. Periods of decreased $r_0$ during specific periods (mid-October and mid-November) may be attributed to meteorological factors, such as cold air activity or strong winds, which enhance turbulence. The overall distribution suggests that seasonal atmospheric turbulence effects were not significantly evident during the observation period.

Figure 11 illustrates the hourly median variations in value of $r_0$ from 16 August to 10 December. The x-axis represents the time of day, while the y-axis denotes the median value of $r_0$ (in centimeters). The mean and median curves illustrate that overall $r_0$ conditions remain relatively stable. Morning values of $r_0$ are generally higher; however, as temperatures rise and the ground heats up, $r_0$ gradually deteriorates, particularly from midday to the afternoon (12:00–15:00), where a noticeable downward trend is observed. This decline is closely associated with increased solar radiation and surface heating, which intensify atmospheric turbulence, leading to greater atmospheric instability and subsequently affecting observation quality.

Figure 12 highlights the typical diurnal pattern of atmospheric turbulence, with $r_0$ values generally improving in the morning and deteriorating as the day progresses. This variation is primarily driven by thermal effects: as the ground warms due to solar radiation, atmospheric instability increases, leading to a decrease in $r_0$ values, which indicates worse seeing conditions. Conversely, as solar heating reduces in the afternoon and evening, the atmosphere stabilizes, resulting in improved $r_0$ values.

5. Discussion

The LP-SDIMM has demonstrated its effectiveness in measuring daytime atmospheric turbulence, providing valuable insights into high-altitude observational sites. The observed diurnal variations in r0 values, characterized by improved seeing in the morning and deteriorating conditions in the afternoon, align with typical thermal effects observed at high-altitude sites. These effects cause greater atmospheric instability during midday, while atmospheric conditions stabilize in the evening.

The LP-SDIMM successfully tracked a wide range of turbulence levels (1.5 cm to 12 cm), highlighting the significant influence of local environmental factors and broader atmospheric conditions on daytime seeing. The system’s ability to continuously monitor these variations further underscores its potential for long-term atmospheric monitoring, which is crucial for optimizing solar telescopes and selecting suitable sites for future astronomical observatories.

However, several limitations were identified. First, ground-level installations may disproportionately capture near-surface turbulence, which is typically more variable than upper atmospheric turbulence. Future deployments should aim to elevate the system above ground level, such as mounting it on towers or platforms, to obtain more representative measurements of the entire atmospheric column. Additionally, the observation campaign was limited to 85 days, restricting our ability to assess seasonal variations in turbulence. A longer observation period, ideally spanning an entire year, would offer more comprehensive data on seasonal changes and their impact on daytime seeing conditions.

Deploying the LP-SDIMM at multiple high-altitude sites will facilitate cross-site comparisons, enabling the identification of regional trends in daytime turbulence. This will enhance the understanding of how atmospheric conditions vary across different geographical regions, helping to optimize site selection for large astronomical observatories. The integration of additional meteorological sensors, such as wind speed, temperature, and humidity, will allow for a more detailed analysis of the factors driving r0 fluctuations, providing a clearer understanding of the physical drivers behind daytime seeing.

Finally, refining the image processing pipeline is critical to improving the system’s real-time capabilities. Enhancing edge detection algorithms and reducing computational latency will enable more efficient high-frequency measurements, which are essential for continuous monitoring at solar observatories.

6. Conclusions

This research introduces the Low-cost Portable Solar Differential Image Motion Monitor (LP-SDIMM) for measuring daytime atmospheric turbulence at astronomical observatories. By integrating a simplified optical path and leveraging computational image analysis, the LP-SDIMM achieves accurate estimations of the Fried parameter (r0) without relying on complex wavefront reconstruction techniques. The system’s compact design and light weight, paired with a low-cost solution, make it ideal for deployment in remote, high-altitude environments.

The performance of the LP-SDIMM was validated through a series of comparative tests with a conventional SDIMM. The results demonstrated strong consistency, with a root mean square error (RMSE) of less than 0.2 cm and a correlation coefficient of 0.92. Field measurements from the Ali Observatory (5050 m elevation) from August to December 2024 yielded a continuous daytime r0 dataset, ranging from 1.5 to 12 cm, with a mean value of 4.09 cm. Temporal analysis revealed typical diurnal trends: superior seeing conditions in the morning and degradation in the afternoon due to solar-induced thermal effects.

While the LP-SDIMM performed well, some limitations were noted, such as the potential overestimation of near-surface turbulence due to its ground-level installation and the relatively short 85-day observation period. Future improvements should focus on:

• Elevating the system to minimize near-surface turbulence interference.
• Extending the observation period to capture a full seasonal cycle.
• Deploying the system at multiple high-altitude sites for comparative analysis.
• Integrating meteorological sensors to correlate atmospheric conditions (e.g., wind, temperature, and humidity) with r0 variations.
• Refining the image processing algorithms to minimize latency and enhance real-time processing.