Clouds are a crucial factor in numerical weather forecasting (NWF), significantly influencing weather-related disasters such as hail, storms, and other extreme conditions. Accurate global measurements of horizontal and vertical distributions of clouds and aerosols, as well as their optical and micro-physical properties, are necessary to assess their influence on human health, the environment, and regional climate and precipitation. The China Meteorological Administration (CMA) and the World Meteorological Organization (WMO) have outlined specific requirements for cloud phase, cloud top height, aerosol extinction coefficient, and measurement error limits. Previous payloads such as CALIPSO (NASA, operational for 17 years) and ACDL (SIOM, operational for over 2 years) have demonstrated partial cloud measurement capabilities. The EarthCARE payload, including the 355 nm HSRL scheme developed by ESA, was launched on May 29, 2024. However, there remains a gap in providing certain-swath, multi-wavelength, multi-scheme, high-precision lidar measurement data.

To quantify the influences of clouds on precipitation, regional climate, and the global environment, we propose the concept of a multi-wavelength, multi-function, multi-beam cloud lidar (M³CL) based on a polar orbit satellite. The M³CL design incorporates high spectral resolution lidar (HSRL), polarization detection, and backscattering detection schemes, with four wavelengths (355, 532, 1064, and 1625 nm) and nine beams in a push-broom configuration, as shown in Fig. 1. Fabry-Perot etalons and iodine cells are used as high spectral resolution filters for 355 and 532 nm channels, respectively. The 355 and 532 nm channels utilize polarization detection, while the remaining eight 532 nm beams are symmetrically arranged on either side of the central beam, forming a 20 km swath. We derive theoretical upper bounds for cloud and aerosol detection errors based on HSRL equations and system calibration constants. Using the parameters set in Table 1, the atmosphere/cloud mode database, and lidar equations, we simulate the SNR distribution for an 820 km polar satellite orbit.

We present simulation results for the relative errors upper limits of cloud and aerosol detection in Figs. 2 and 3. The backscattering coefficient relative error is below 17.6% with an SNR higher than 20, and within 31.2% with an SNR higher than 10. Sensitivity simulations for different detection wavelengths in Fig. 4 show that the M³CL can semi-quantitatively determine particle radii ranging from 0.2 to 2 μm. Figs. 5 and 6 indicate that the SNR exceeds 20 under thin cloud and weak scattering conditions, while Figs. 7 and 8 demonstrate that SNR remains above 20 under 2 km thick cloud, intense scattering conditions. Figs. 9 and 10 indicate that aerosol detection SNR is about 10. The SNR distribution figures reveal that cloud detection SNR exceeds 20 at a 2.5 km horizontal resolution and a 200 m vertical resolution, resulting in a relative detection error within 20%. The penetration depths for thick and thin clouds are over 300 and 1000 m, respectively.

We present the concept of the M³CL payload with system parameters based on a new generation polar satellite, featuring three detection schemes, four wavelengths, and nine beams. The M³CL is capable of push-broom measurements with a 20 km swath, 2.5 km horizontal resolution, and 200 m vertical resolution. We provide theoretical upper limits for particle backscattering coefficient detection errors based on HSRL theory, serving as a reference for evaluating HSRL detection errors. Simulation results indicate that the M³CL can achieve a cloud backscattering coefficient detection relative error within 20%, calculate particle radii from 0.2 to 2 μm, and penetrate thick and thin clouds to depths exceeding 300 and 1000 m, respectively. These capabilities meet the cloud detection requirements of meteorological satellites.