Next Article in Journal
The Suitability of a Remote Microwave Radiometer for Detecting Volcanic Activity
Previous Article in Journal
Spatial-Temporal Distribution and Microphysical Characteristics of Aerosols and Clouds over China: A Combined Satellite and Aircraft Observation Study
Previous Article in Special Issue
Regional Variability in the Structure and Microphysical Characteristics of Hail Clouds over China Based on GPM Observations and ERA5 Reanalysis
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Remote Sensing
Volume 18
Issue 16
10.3390/rs18162798
remotesensing-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Recent Progress in Remote Sensing of Clouds and Precipitation Physics: Platforms, Applications, and Emerging Frontiers

by
Zuhang Wu
1,*,
Long Wen
2,
Yong Zeng
3 and
Ismail Gultepe
4,5,6
1
College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China
2
Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
3
Institute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, China
4
Engineering and Applied Science Department, Ontario Technical University, Oshawa, ON L1G 0C5, Canada
5
Department of Aeronautical Engineering, Faculty of Aeronautics and Astronautics, University of Turkish Aeronautical Association, Ankara 06790, Türkiye
6
Department of Civil and Environmental Engineering and Earths Sciences, University of Notre Dame, Notre Dame, IN 46556, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2026, 18(16), 2798; https://doi.org/10.3390/rs18162798
Submission received: 3 July 2026 / Revised: 10 August 2026 / Accepted: 18 August 2026 / Published: 19 August 2026
(This article belongs to the Special Issue Remote Sensing in Clouds and Precipitation Physics)
Browse Figures
Versions Notes

Highlights

What are the main findings?
  • Development and coordination of multi-platform remote sensing observation system.
  • Evolution of remote sensing retrieval methods for clouds and precipitation characteristics.
  • Remote sensing characterization capability of clouds and precipitation formation mechanism.
What are the implications of the main findings?
  • The integrated multi-platform remote sensing framework improves the observation capability of clouds and precipitation.
  • Advanced retrieval methods provide more accurate characterization of clouds and precipitation.
  • Enhanced remote sensing capabilities contribute to better understanding of cloud–precipitation processes and formation mechanisms.

Abstract

Remote sensing of clouds and precipitation is undergoing a significant transition from descriptive observations toward process-oriented diagnoses. This transition has not progressed linearly, but has gradually taken place alongside the rapidly developing multi-source observation capabilities over recent decades. In terms of clouds and precipitation observational platforms, satellites provide continuous global-scale monitoring, airborne platforms complement high-resolution sampling of key processes, and ground-based observations offer long-term vertical structure evolution, which form an integrated space–air–ground observation system. In terms of clouds and precipitation retrieval algorithms, active–passive combination remote sensing significantly improves the ability to retrieve macro- and microphysical characteristics and structures, and machine learning methods further expand parameter estimation capabilities in complex scenarios. Nevertheless, key bottlenecks still persist in retrieval non-uniqueness, sensor trade-offs, cross-platform calibration, and validation over oceans, mountains, and polar regions. Based on the above background, this paper provides a systematic review of recent progress in clouds and precipitation physics remote sensing, focusing on the development of multi-platform collaborative observations, the evolution of microphysical parameter retrieval methods, and the improvements in remote sensing characterization of cloud and precipitation formation mechanisms. It further points out that future development will increasingly rely on improved uncertainty quantification, incorporation of physical constraints into retrieval frameworks, and the establishment of standardized multi-source datasets.

1. Introduction

Clouds and precipitation are one of the most crucial and challenging components to depict in the Earth system, posing impacts on weather patterns, hydrological cycle, and radiative energy balance [1,2,3]. Accurate observation of these phenomena is crucial for the comprehension of atmospheric physical processes, the enhancement of weather prediction capabilities, and the projection of climate change impacts [4,5,6].
Conventional observation methods, such as precipitation gauges and radio/dropsondes, have provided valuable insights but are limited by spatial coverage, particularly over oceans and remote regions [7,8,9]. Moreover, these observations can hardly provide direct information on the three-dimensional structure and phase evolution within clouds and precipitation, leading to a long-term reliance on indirect inferences for cloud and precipitation processes [9]. These limitations impede comprehensive understanding and modeling of cloud-precipitation processes.
The emergence of remote sensing technologies, covering from satellite to airborne to ground-based platforms, has revolutionized atmospheric observation, facilitating global, high-resolution, and multi-parameter monitoring of clouds and precipitation [10,11,12]. Meanwhile, the retrieval algorithm has also undergone significant evolution, from the early empirical retrieval based on a single sensor, into a method system combining active–passive collaboration, multi-source constrained retrieval, and the integration of data-driven and physical models [13,14,15]. These advances have led to significant progress in retrieving macro- and microphysical cloud characteristics, understanding vertical structures, and unraveling formation mechanisms.
However, it has also introduced new issues, including increased observation inconsistency, complicated uncertainty propagation, and decreased physical interpretability. These issues are particularly prominent in mixed-phase clouds, solid precipitation, and severe convective systems. In these systems, the coexistence, rapid evolution, and scale spanning of various hydrometeor species make it difficult to fully characterize their structure and evolutionary path through single-platform observations, thus limiting our in-depth understanding of the precipitation formation mechanisms. Therefore, an important shift from “variable retrieval priority” to “process identification priority” is happening in current research. The focus is no longer only on retrieving parameters such as cloud water content or precipitation rate, but rather on further inquiring into how these structures form, evolve, and trigger precipitation.
As a contribution to the Special Issue “Remote Sensing in Clouds and Precipitation Physics”, this review summarizes recent progress in remote sensing of clouds and precipitation physics from a process-oriented perspective. It highlights how satellite, airborne, and ground-based remote sensing observations increasingly work together to retrieve key macro- and microphysical properties, and reveal cloud-precipitation formation mechanisms. Meanwhile, the current challenges and emerging frontiers are also discussed.

2. Remote Sensing Platforms and Instruments

2.1. Satellite Remote Sensing

Satellite observations can routinely capture clouds and precipitation on a global scale, and thus remain the backbone of climatological analysis, cross-basin comparison, and model evaluation. Their main strengths are broad coverage, repeated sampling, and mature long-term data archives, whereas their main limitations consist of footprint size, revisit constraints, and reduced retrieval accuracy in vertically complex or rapidly evolving weather systems [12].
Passive imagers and sounders in the visible, infrared, and microwave bands retrieve cloud fraction, optical thickness, effective radius, cloud-top properties, liquid/ice water path, and surface precipitation. A key point worth further illustration is the complementarity between polar-orbiting and geostationary systems. Polar platforms such as Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), and Advanced Technology Microwave Sounder (ATMS) provide consistent global products [16,17,18]. Geostationary imagers such as Flexible Combined Imager (FCI), Advanced Baseline Imager (ABI), Advanced Himawari Imager (AHI), and Advanced Geosynchronous Radiation Imager (AGRI) add the temporal detail required to track convective initiation, diurnal variation, and rapidly evolving cloud microphysical characteristics [19,20,21,22].
Active sensors incorporate the vertical dimension that passive observations are unable to directly resolve. CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) have established the benchmark for profiling cloud layers, cloud phase, and cloud-aerosol vertical structure [23,24,25], whereas Global Precipitation Measurement (GPM) Dual-frequency Precipitation Radar (DPR), and Fengyun-3G (FY-3G) Precipitation Measurement Radar (PMR) provide three-dimensional precipitation structure and raindrop size distribution information indispensable for microphysical studies of monsoons, typhoons, and extreme rainfall events [26,27]. These missions are of particular utility for connecting column-integrated signals to process-level vertical organization. However, active remote sensing suffers from limited spatial coverage and relatively low temporal continuity as compared with passive remote sensing, rendering it incapable of effectively compensating for vertical information deficiencies from passive remote sensing. Moreover, several active missions are reaching or have reached the end of their operational lifetimes, leading to reduced availability of continuously updated observational datasets.
Current satellite development is therefore moving toward multi-sensor synergy, higher revisit frequencies, and more intelligent retrieval. The Earth Clouds, Aerosols, and Radiation Explorer (EarthCARE) satellite represents an important step by integrating radar, lidar, imager, and broadband radiometry to constrain clouds, aerosols, precipitation, and radiation at the same time [28,29,30]. Meanwhile, advances in artificial intelligence (AI) have introduced new possibilities for reconstructing three-dimensional cloud structures from dense two-dimensional satellite imagery [31,32], or for broadening the spatial coverage of active remote sensing through the integration of passive remote sensing [33,34]. Accordingly, active–passive combination, machine-learning retrievals, and multi-satellite merged precipitation products are now as important as the payload itself, since they translate heterogeneous measurements into physically consistent datasets of clouds and precipitation.

2.2. Airborne Remote Sensing

Airborne platforms occupy the pivotal intermediate scale between satellites and surface sites. Their main value lies in the targeted, high-resolution sampling of specific weather systems, with the unique capability to integrate remote sensing with in-situ probes on the same platform. This makes aircraft irreplaceable for studies of cloud physical processes, underflight validation of satellite products, as well as the development of retrieval algorithms and model parameterizations [35].
A concise instrument grouping is sufficient here. Active airborne sensors such as W/Ka-band cloud radars and lidars resolve cloud boundaries, hydrometeor layering, and vertical kinematics; passive radiometers, infrared imagers, and polarimeters add thermodynamic and radiative constraints; and in-situ probes directly measure particle size, phase, shape, and concentration. Representative field campaigns such as the Midlatitude Continental Convective Clouds Experiment (MC3E) [36], Convective Processes Experiment (CPEX) [37], Next-generation Aircraft Remote-sensing for VALidation studies (NARVAL) [38], Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) [39], Arctic CLoud Observations Using airborne measurements during polar Day (ACLOUD) [40], and Tibetan Plateau aircraft experiments [41] exhibit how these measurements reveal entrainment process, mixed-phase structure, snowfall growth, aerosol–cloud interaction, and deep convective microphysics.
The main limitation of aircraft observations is not capability, but representativeness. Constraints associated with flight duration, operational costs, and safety considerations restrict spatial and temporal coverage, while the most intense regions of severe storms remain challenging to sample directly. Due to that, airborne observations are most appropriately categorized as calibration-quality, mechanism-oriented datasets rather than standalone monitoring systems. A missing but important emerging direction is the growing role of unmanned aerial vehicles (UAVs), which can extend atmospheric observations into hazardous, remote, or fine-scale environments where conventional aircraft measurements are difficult to conduct [42,43,44].

2.3. Ground-Based Remote Sensing

Ground-based platforms provide the temporal continuity and vertical profiling capability that neither satellites nor aircraft can fully deliver. Cloud radars, polarimetric weather radars, micro rain radars, lidars, microwave radiometers, wind profilers, disdrometers, and all-sky imagers together resolve the evolution of cloud and precipitation systems from minutes to years, making them the primary benchmark for process-oriented validation and long-term monitoring [45].
For cloud physics, Ka/W-band cloud radars and lidars are especially significant for profiling boundary-layer clouds, cirrus, and mixed-phase layers, whereas polarimetric weather radars serve as the core tool for precipitation intensity estimation, hydrometeor classification, and near-surface microphysical retrieval [46]. Micro rain radars measure vertically-oriented precipitation parameters, while microwave radiometers provide continuous liquid water content and thermodynamic constraints [47]. Wind profilers complement these observations by providing continuous vertical profiles of horizontal wind and Doppler velocity, enabling the characterization of atmospheric circulation, convergence zones, and storm-scale vertical motions that regulate cloud formation and precipitation development [48,49]. Disdrometers and precipitation gauges remain indispensable reference instruments for raindrop and snowfall characterization at the surface [50], which are very important as they address one of the persistent blind zones of spaceborne and profiling radar observations.
Networked observations significantly boost the utility of individual instruments. Integrated multi-instrument networks, such as the Atmospheric Radiation Measurement (ARM) [51] and the Aerosol, Clouds, and Trace Gases Research Infrastructure (ACTRIS) [52], provide long-term standardized benchmark datasets. Operational weather radar networks have become essential components of modern clouds and precipitation observing systems, including the Next Generation Weather Radar (NEXRAD) in the United States [53], the China New Generation Weather Radar (CINRAD) in China [54], the Operational Program on the Exchange of Weather Radar Information (OPERA) across Europe [55], and the eXtended Radar Information Network (XRAIN) in Japan [56]. It is also notable that the recent emergence of opportunistic sensing, such as commercial microwave link (CML), is capable of improving urban and near-surface precipitation monitoring where conventional radar coverage is limited [57].

2.4. Space–Air–Ground Integration

No single platform has the capacity to simultaneously provide global coverage, complete vertical structure, high revisit frequencies, direct macro-/microphysical truth, and long-term continuity. This limitation arises from the different sampling strategies and sensing mechanisms of satellite, airborne, and ground-based platforms. Satellites provide broad spatial coverage and long-term consistency via passive radiometric measurements and spaceborne active sensors, but their retrievals are often indirect and may be insufficient to resolve fine-scale vertical structures and rapid microphysical evolution. In contrast, airborne and ground-based observations provide high-resolution measurements through active remote sensing and in-situ sampling, thus offering detailed information on cloud dynamics, hydrometeor properties, and precipitation processes. However, their limited spatial representativeness restricts their applications in localized or targeted research.
Therefore, the application value of high-resolution ground and in-situ observations is highly dependent on specific scenarios. Such observations are particularly important for process-oriented research, including cloud formation mechanisms, precipitation microphysics, severe weather evolution, retrieval algorithm validation, and numerical model evaluation. In these studies, detailed local measurements are required to constrain physical processes. Conversely, satellite observations are more suitable for large-scale applications, such as global precipitation monitoring, climate change assessment, hydrological analysis, and long-term trend detection, where spatial coverage and temporal consistency are more important than fine-scale details. Therefore, satellite, airborne, and ground-based observations should be regarded as complementary rather than competing methods. Satellites provide scale and consistency, aircraft provide targeted process measurements and validation, and ground-based systems provide continuous evolution and surface truth [58,59]. Table 1 summarizes the major remote sensing platforms, representative instruments, and their respective strengths and limitations as illustrated above. Figure 1 elaborates on the complementary roles of satellite, airborne, and ground-based observations; integrating these observations through cross-platform calibration, uncertainty characterization, and physically-constrained data fusion is a key direction for improving cloud and precipitation retrievals.

3. Remote Sensing of Cloud and Precipitation Characteristics

Remote sensing technology has enabled routine retrieval of key cloud and precipitation properties across scales, but the main advance is no longer the separate estimation of individual variables. Current progress comprises the integration of passive and active observations from satellite, airborne, and ground-based platforms to retrieve mutually constrained macro- and microphysical parameters, characterize their associated uncertainties, and relate them to radiation, dynamics, and model physical parameterizations [60].

3.1. Cloud Optical Thickness, Effective Radius, and Phase

Cloud optical thickness (COT) and effective radius (CER) remain foundational variables for cloud radiative effect studies and for diagnosing aerosol–cloud interaction [61]. The classic bispectral framework established the basis for global retrievals from MODIS and VIIRS, while new-generation geostationary imagers such as FCI, ABI, AHI, and AGRI add the temporal resolution needed to track convective initiation and rapid cloud evolution [62,63].
Recent work has moved beyond standard homogeneous-cloud assumptions. Multi-angle, polarized, and machine-learning-assisted retrievals have improved performance for broken clouds, mixed-phase systems, and bright surfaces, while active–passive combination has strengthened cloud-phase discrimination and reduced ambiguity between optical and microphysical signals [64]. It should be noted that phase partitioning is now as important as COT and CER themselves, because errors in liquid-versus-ice attribution propagate directly into cloud water path, radiative forcing, and precipitation-onset diagnostics [65,66].

3.2. Liquid/Ice Water Content and Vertical Hydrometeor Structure

Liquid water content (LWC), ice water content (IWC), and their column-integrated counterparts provide a direct link between cloud thermodynamics and precipitation formation. Passive microwave sensors such as ATMS are effective for all-weather retrieval of liquid and frozen hydrometeors at large scales, whereas cloud radar–lidar–radiometer combinations provide the vertical detail required for boundary-layer clouds, cirrus, and mixed-phase layers [67]. These observing approaches usually target different spatial scales and scientific objectives, but they can complement each other through appropriate spatiotemporal matching, scale adjustment, and data fusion techniques.
The major advance in this area is the use of synergistic retrievals rather than single-instrument products. Ground-based millimeter cloud radar combined with microwave radiometers constrains LWC and IWC profiles continuously, and airborne in-situ observations remain essential for evaluating solid precipitation particle habit, density, and retrieval bias [68]. This has been particularly important for cirrus and mixed-phase clouds, where IWC retrieval uncertainty remains large and where supercooled liquid water strongly controls subsequent riming and ice growth processes.

3.3. Precipitation Rate, Particle Size Distribution, and Near-Surface Constraints

Remote sensing has also transformed the retrieval of precipitation rate and particle size distribution (PSD), especially through the combination of spaceborne dual-frequency radar and ground-based polarimetric radar. GPM DPR, and FY-3G PMR provide global three-dimensional precipitation structure and constrain the PSD parameters such as mass-weighted mean diameter (Dm) and normalized intercept parameter (Nw), while polarimetric radar refines hydrometeor classification and near-surface microphysics where satellite sensitivity is limited [26,27,61].
These datasets have clarified regional contrasts in raindrop spectra, including differences between maritime typhoon rainfall and continental convection, and have improved quantitative precipitation estimation in extreme rain events [69,70,71]. An important missing point in many summaries is that surface reference observations (especially disdrometers and gauges) remain indispensable, because they anchor PSD retrievals from both ground-based and spaceborne radars and help diagnose evaporation, breakup, and snowfall errors near the ground [45,72,73]. Machine-learning approaches are increasingly useful here, but their value is greater when they are constrained by physically interpretable radar variables and surface truth rather than used as purely black-box estimators [74].
To synthesize the main variables discussed above and clarify their observational basis, Table 2 summarizes the key cloud and precipitation properties, their primary observing systems, and their main strengths and limitations. This comparison also provides a useful bridge from variable retrieval in Section 3 to the process-oriented analyses in Section 4.

4. Remote Sensing of Cloud and Precipitation Processes

In addition to parameter retrieval, remote sensing has become central to process-oriented investigations of clouds and precipitation physics. The strongest contribution is the capability of observing how vertical structure, phase transition, and hydrometeor growth evolve through time, thereby connecting dynamics and microphysics across warm-rain, mixed-phase, and deep-convective regimes.

4.1. Cloud Vertical Structure, Entrainment, and Mixed-Phase Evolution

Cloud radar and lidar observations from CloudSat/CALIPSO, ground-based profiling systems, and aircraft underflights have verified vertical structure as a primary diagnostic for cloud development, entrainment, and precipitation initiation [24]. For example, entrainment can be inferred from enhanced radar spectral width, which reflects increased turbulence and velocity variability, together with lidar backscatter gradients and depolarization-ratio changes associated with the incorporation of dry environmental air. These observations demonstrate how entrainment alters droplet spectra, cloud-top cooling, and the probability of warm-rain formation, while in mixed-phase clouds the coexistence of supercooled liquid water and ice governs riming, depositional growth, and cloud lifetime [75,76,77].
Recent advances suggest that the greatest strength of remote sensing lies in distinguishing different process regimes rather than merely characterizing mean properties [78]. This includes differentiating stratocumulus from cumulus mixing, identifying snowfall growth dominated by vapor deposition or riming, and separating shallow warm-rain processes from deep ice-assisted precipitation pathways [79]. Compared with individual case statistics, such a regime-based approach offers more valuable guidance for the development and evaluation of model physical parameterizations.

4.2. Precipitation Initiation and Hydrometeor Growth Pathways

Multi-platform observations have significantly improved understanding of precipitation formation mechanisms. In warm clouds, radar and radiometer observations facilitates the recognition of the transition from condensational growth to collision-coalescence process by analyzing vertical variations in raindrop diameter and concentration. In cold and mixed-phase clouds, joint observations of radar, lidar, and aircraft resolves the roles of ice nucleation, aggregation, riming, and melting processes. High-temporal-resolution polarimetric radar and phased array radar (PAR) observations further reveal how hydrometeor types and PSD evolve within convective updrafts, squall lines, and typhoon rainbands [70,78,80].
More attention should be paid to solid precipitation, a phenomenon that still receives insufficient coverage in a large number of studies [81]. Recent observational studies demonstrate that riming process can strongly increase snow particle size and snowfall intensity, and that polarimetric radar variables can be quantitatively related to the extent of riming, offering a pathway toward improved quantitative snowfall estimation and ice-phase process parameterization [82,83,84].

4.3. Extreme Weather, Model Evaluation, and Climate Applications

Remote sensing has become indispensable for analyzing extreme events such as typhoons, mesoscale convective systems, extreme urban rainstorms, and snowstorms. Multi-sensor joint observations have revealed microphysical contrasts between convective and stratiform precipitation [85], clarified the role of warm-rain growth in landfalling tropical cyclones [58,86,87], and documented how dynamics and microphysics interact during tropical cyclone rapid intensification [88,89], thunderstorm convective impulse [90], and extreme rain events [91,92,93,94,95].
Cloud and precipitation structures derived from remote sensing also provide benchmarks for evaluating bulk/bin microphysics schemes in numerical models, enhancing numerical weather prediction through data assimilation and observation constraint, improving quantitative precipitation estimation, and assessing the influence of cloud seeding [96,97,98,99]. At climate scales, long-term datasets of cloud phase, water content, optical properties, and precipitation characteristics are essential for evaluating cloud feedbacks, aerosol–cloud–precipitation interaction, and regional hydrological change [100].
To provide a process-oriented synthesis, Table 3 summarizes the key mechanisms, observational signatures, and corresponding remote sensing constraints. Overall, the application focus of remote sensing in clouds and precipitation physics is shifting from description toward interpretation. Rather than only retrieving variables, growing attention is being paid to identifying process pathways, quantifying uncertainty, and improving forecast and climate models. That shift should be stated explicitly because it best captures the current frontier of the field.

5. Challenges and Frontiers

5.1. Current Challenges

Remote sensing of clouds and precipitation physics is now less limited by data availability than by four persistent bottlenecks, including retrieval ambiguity, sensor trade-offs, poor observability of mixed-phase and rapidly evolving systems, and weak validation in extreme environments. These issues directly constrain process studies, model evaluation, and climate applications, making Section 5 less a list of technical problems than a roadmap for the next stage of this field.

5.1.1. Uncertainties in Microphysical Parameter Retrieval Algorithms

The main algorithmic challenge is non-uniqueness, since different cloud and precipitation states can produce similar radiometric or radar signatures. Retrieval uncertainties therefore primarily arise from the idealized assumptions about prescribed PSD, particle shape, density, and cloud homogeneity approximations embedded within retrieval frameworks. This circumstance poses a particularly prominent obstacle for the quantification of ice water content, light precipitation, and snowfall over complex terrain, where uncertainties remain sufficiently large to limit applications in hydrological and numerical modeling studies [101,102].

5.1.2. Intrinsic Limitations of Sensors and Physical Constraints

The limitations of sensors themselves remain fundamental. Passive visible and infrared sensors largely observe cloud-top properties, whereas microwave sensors procure penetrative capacity at the cost of spatial resolution. Active sensors encounter the opposite trade-off. Radars operating at Ka/W bands are sensitive to small particles, but undergo strong attenuation in heavy precipitation, while longer-wavelength (S/C/X bands) weather radars achieve deeper penetration but fail to capture weak cloud signals. Active–passive combination introduces further uncertainties from footprint mismatch, sampling differences, and error propagation. Moreover, multiple scattering, nonuniform beam filling, attenuation, and the persistent near-surface blind zone also impede the retrievals of evaporation, breakup, melting, and surface precipitation [103,104,105,106].

5.1.3. Observation Bottlenecks in Complex Cloud-Precipitation Systems

The most difficult targets for observations encompass mixed-phase clouds, solid precipitation, and rapidly evolving deep convection. In mixed-phase clouds, supercooled liquid water, ice, snow, and graupel coexist on fine vertical scales, yet no solitary instrument is capable of resolving phase partitioning and growth pathways with adequate precision [107,108]. As for severe convective systems, their microphysical characteristics evolve at minute-scale time steps that usually outpace routine radar scans and satellite sampling [109]. These observational gaps still impose constraints on the comprehension of precipitation initiation, riming process, snowfall growth, and extreme rainfall production.

5.1.4. Multi-Platform Data Synergy and Validation Challenges in Extreme Environments

Data validation is also uneven across the world. Polar regions, mountain areas, oceans, and arid deserts suffer from insufficient sampling, so retrieval errors are usually most prominent exactly where climate sensitivity is high and ground truth is scarce. Over these regions, snowfall, blowing snow, radar beam blockage, surface heterogeneity, and marine validation gaps all complicate the evaluation across different platforms [110]. An additional yet commonly undernoted issue is the lack of standardized uncertainty metrics, unified data formats, and benchmark datasets across satellite missions, field campaigns, and ground networks, which decelerates reproducible data fusion and intercomparison [111].

5.2. Frontier Technologies

The main frontiers are therefore not only new instruments and platforms, but also new observing and retrieval strategies. This includes uncertainty-aware retrieval, physics-guided AI, calibrated space–air–ground fusion, and tighter integration of observations with numerical models. Progress will rely on whether the community can convert heterogeneous measurements into physically consistent, benchmarked, and operationally useful cloud and precipitation products. Ultimately, these advances will not only enhance our ability to observe and characterize clouds and precipitation, but also deepen our understanding of the underlying microphysical and dynamical processes, thereby leading to a more process-level understanding of clouds and precipitation physics.

5.2.1. AI-Driven Intelligent Retrieval Technology

AI has emerged as the most valuable tool where traditional retrieval is weakest in terms of broken clouds, mixed-phase scenes, solid precipitation, and multi-sensor fusion [112]. Recent developments have shifted AI-based retrieval from purely data-driven prediction to physics-guided, uncertainty-aware retrieval that incorporates radiative transfer, hydrometeor constraints, and conservation laws to improve reliability and interpretability [113,114,115]. Meanwhile, the newest AI architectures, such as state space models like “Mamba”, are highly effective for rapid cloud and precipitation nowcasting [116,117]. Future developments may also include quantum-inspired three-dimensional cloud retrieval and precipitation forecasting [118]. From this perspective, the frontier lies in developing hybrid retrieval frameworks that combine advanced AI architectures with physical constraints to achieve accurate, interpretable, and generalizable cloud-precipitation observations.

5.2.2. Space–Air–Ground Integrated Multi-Source Data Fusion Technology

The integration of space, air, and ground observations has undergone a gradual shift from simple product merging to physically-constrained integration across scales. Active–passive combination, Bayesian and variational retrieval, multi-satellite precipitation fusion, and cross-scale matching now make it possible to connect global context, process-scale aircraft observations, and continuous ground truth within a common framework. An important next step is to fuse not only observations, but also their uncertainties, so that retrievals can be assimilated more directly into numerical weather prediction, process models, and ultimately digital-twin-style Earth system applications [119,120].

5.2.3. Next-Generation Satellite Missions and Advanced Observation Payloads

Next-generation missions and payloads will expand what can be observed directly. EarthCARE has set a new benchmark for coordinated radar–lidar–imager–radiometer observation, while the Second Generation of European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Polar System (EPS-SG) [121], the Plankton, Aerosol, Cloud, Ocean Ecosystem (PACE) [122], and the Fengyun geostationary microwave satellite [123] will improve cloud phase, aerosol–cloud interaction, precipitation structure, and temporal continuity. At the payload level, multi-band radar, submillimeter radiometers, and advanced polarimeters are especially promising for light precipitation, ice microphysics, and the early stages of warm-rain formation [124]. The real frontier, however, lies in how these sensors are cross-calibrated, intercompared, and translated into stable long-term data records rather than treated as isolated missions.

5.2.4. Emerging Near-Ground Observation Technologies and Cross-Disciplinary Applications

Emerging near-ground observing systems, including CML, PAR, UAV-based sampling, and other novel sensors, are extending observations into urban, hazardous, and fine-scale environments that conventional platforms sample poorly [125,126,127,128]. At the same time, cloud and precipitation remote sensing is becoming more tightly linked with hydrology, climate diagnostics, disaster early warning, and model assimilation. This cross-disciplinary integration should be viewed as a frontier in its own right, as remote sensing is moving from purely descriptive observation toward process interpretation, forecast constraint, and decision support.

6. Conclusions

To summarize the entire paper, Figure 2 presents a conceptual schematic connecting remote sensing observations, retrieval strategies, and cloud-precipitation physical processes, highlighting current challenges and emerging frontiers.
Overall, remote sensing has moved clouds and precipitation physics from sparse description to multi-scale, process-aware observation. The major advance is not simply more data, but the growing ability to connect global satellite coverage, aircraft process studies, and continuous ground-based profiling into physically consistent views of cloud structure, phase evolution, and precipitation formation. As a result, remote sensing is now increasingly capable of diagnosing mixed-phase processes, constraining warm-rain and ice-growth pathways, and providing benchmarks for model development, data assimilation, and climate evaluation.
Meanwhile, the central limitations of the field are now clear, including retrieval ambiguity, fundamental sensor trade-offs, inadequate sampling of mixed-phase and rapidly evolving systems, and insufficient benchmark observations in oceans, mountains, and polar regions. Future progress will rely on several key priorities, including the development of physics-guided and uncertainty-aware retrieval frameworks, the establishment of standardized cross-platform datasets with comprehensive validation and error quantification, and the more direct integration of observations with numerical weather prediction, hydrological applications, and climate models.
Finally, this review mainly synthesizes the literature over the past decade to provide an overview of recent progress in remote sensing of clouds and precipitation physics. Considering the rapidly evolving nature of this field, some aspects may not have been fully covered. Nevertheless, we hope this review will serve as a useful reference for researchers in remote sensing instrument development, retrieval algorithm advancement, cloud and precipitation characteristics analysis, physical mechanism investigation, climate statistics, model evaluation, and applications of AI technologies.

Author Contributions

Conceptualization, Z.W.; methodology, Z.W.; software, Z.W.; validation, Z.W.; formal analysis, Z.W.; investigation, Z.W.; resources, Z.W.; data curation, Z.W.; writing—original draft preparation, Z.W.; writing—review and editing, Z.W., L.W., Y.Z., and I.G.; visualization, Z.W. and L.W.; supervision, Z.W.; project administration, Z.W.; funding acquisition, Z.W. All authors have read and agreed to the published version of the manuscript.

Funding

This work was financially supported under grants from the National Natural Science Foundation of China (42305081), the Science and Technology Innovation Program of Hunan Province (2024RC3141), the Shanghai Typhoon Research Foundation (TFJJ202304), and the Research Project of the National University of Defense Technology (ZK23-55).

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article. All of the figures and tables in the review were made professionally by human designers.

Acknowledgments

The authors would like to thank Balaji Kumar Seela for helpful discussions on topics related to this review.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Zhou, R.; Dou, T.; Zhou, C.; Tan, I.; Chen, D.; Xu, G.; Yang, Y.; Gong, X.; Ju, X.; Wang, A.; et al. Observational constraints from global ice-phase fraction indicate moderate climate sensitivity. Sci. Adv. 2026, 12, eaea0731. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  2. Levizzani, V.; Kidd, C. Clouds and Precipitation in the Earth System. In Precipitation; Geophysics and Environmental Physics; Springer: Cham, Switzerland, 2025; pp. 1–11. [Google Scholar]
  3. Freeman, S.; Brunner, K.; Jones, W.; Kukulies, J.; Senf, F.; Stier, P.; van den Heever, S. Advancing Our Understanding of Cloud Processes and Their Role in the Earth System through Cloud Object Tracking. Bull. Am. Meteorol. Soc. 2024, 105, E297–E299. [Google Scholar] [CrossRef] [Scilit]
  4. Dance, S.; Nichols, N. Improving Weather Forecasts Through Advanced Use of Observations. In More UK Success Stories in Industrial Mathematics; Mathematics in Industry; Aston, P.J., Ed.; Springer: Cham, Switzerland, 2025; Volume 42, pp. 9–15. [Google Scholar]
  5. Han, W.; Yin, R.; Li, J.; Liu, Y.; Wang, J.; Li, Y.; Qin, X.; Zhang, Z.; Shen, X. Targeted sounding observations from geostationary satellite and impacts on high impact weather forecasts. Sci. China Earth Sci. 2025, 68, 963–976. [Google Scholar] [CrossRef] [Scilit]
  6. Norris, J.R.; Allen, R.J.; Evan, A.T.; Zelinka, M.D.; O’dell, C.W.; Klein, S.A. Evidence for climate change in the satellite cloud record. Nature 2016, 536, 72–75. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  7. Wu, Z.; Li, B.; Ge, H.; Yuan, L.; Liu, Y. Satellite-Based Remote Sensing of Atmospheric Water Vapor Over Oceans: An Inter-Comparison Against Shipborne GNSS Observations. IEEE Trans. Geosci. Remote Sens. 2024, 62, 4107308. [Google Scholar] [CrossRef] [Scilit]
  8. Yadav, K.; Malviya, S.; Tiwari, A.K. Improving Weather Forecasting in Remote Regions Through Machine Learning. Atmosphere 2025, 16, 587. [Google Scholar] [CrossRef] [Scilit]
  9. Su, J.; Miao, C.; Zwiers, F.; Beck, H.; Jones, P.; Sun, Q.; Slater, L.J.; Berghuijs, W.R.; Wada, Y.; Rosenfeld, D.; et al. Precipitation observing network gaps limit climate change impact assessment. Nature 2026, 652, 119–125. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  10. Asutosh, A.; Chatterjee, S.; Subeesh, M.; Radhakrishnan, A.; Murukesh, N. Observation of Cloud Base Height and Precipitation Characteristics at a Polar Site Ny-Ålesund, Svalbard Using Ground-Based Remote Sensing and Model Reanalysis. Remote Sens. 2021, 13, 2808. [Google Scholar] [CrossRef] [Scilit]
  11. Wehr, T.; Kubota, T.; Tzeremes, G.; Wallace, K.; Nakatsuka, H.; Ohno, Y.; Koopman, R.; Rusli, S.; Kikuchi, M.; Eisinger, M.; et al. The EarthCARE mission–science and system overview. Atmos. Meas. Tech. 2023, 16, 3581–3620. [Google Scholar] [CrossRef] [Scilit]
  12. Levizzani, V.; Cattani, E. Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate. Remote Sens. 2019, 11, 2301. [Google Scholar] [CrossRef] [Scilit]
  13. Shang, H.; Hioki, S.; Penide, G.; Cornet, C.; Letu, H.; Riedi, J. Establishment of an analytical model for remote sensing of typical stratocumulus cloud profiles under various precipitation and entrainment conditions. Atmos. Chem. Phys. 2023, 23, 2729–2745. [Google Scholar] [CrossRef] [Scilit]
  14. Mason, S.; Hogan, R.; Bozzo, A.; Pounder, N. A unified synergistic retrieval of clouds, aerosols, and precipitation from EarthCARE: The ACM-CAP product. Atmos. Meas. Tech. 2023, 16, 3459–3482. [Google Scholar] [CrossRef] [Scilit]
  15. Li, J.; Zhang, F.; Li, W.; Tong, X.; Pan, B.; Li, J.; Lin, H.; Letu, H.; Mustafa, F. Transfer-Learning-Based Approach to Retrieve the Cloud Properties Using Diverse Remote Sensing Datasets. IEEE Trans. Geosci. Remote Sens. 2023, 61, 4106210. [Google Scholar] [CrossRef] [Scilit]
  16. Xiong, X.; Butler, J.J. MODIS and VIIRS Calibration History and Future Outlook. Remote Sens. 2020, 12, 2523. [Google Scholar] [CrossRef] [Scilit]
  17. Xiong, X.; Sun, J.; Angal, A.; Wilson, T. Calibration Inter-Comparison of MODIS and VIIRS Reflective Solar Bands Using Lunar Observations. Remote Sens. 2022, 14, 4754. [Google Scholar] [CrossRef] [Scilit]
  18. Kim, E.; Lyu, C.-H.J.; Anderson, K.; Leslie, R.V.; Blackwell, W.J. S-NPP ATMS instrument prelaunch and on-orbit performance evaluation. J. Geophys. Res. Atmos. 2014, 119, 5653–5670. [Google Scholar] [CrossRef] [Scilit]
  19. Holmlund, K.; Grandell, J.; Schmetz, J.; Stuhlmann, R.; Bojkov, B.; Munroand, R. Meteosat Third Generation (MTG): Continuation and Innovation of Observations from Geostationary Orbit. Bull. Am. Meteorol. Soc. 2021, 102, E990–E1015. [Google Scholar] [CrossRef] [Scilit]
  20. Schmit, T.J.; Griffith, P.; Gunshor, M.M.; Daniels, J.M.; Goodman, S.J.; Lebair, W.J. A Closer Look at the ABI on the GOES-R Series. Bull. Am. Meteorol. Soc. 2017, 98, 681–698. [Google Scholar] [CrossRef] [Scilit]
  21. Letu, H.; Yang, K.; Nakajima, T.; Ishimoto, H.; Nagao, T.; Riedi, J.; Baran, A.J.; Ma, R.; Wang, T.; Shang, H.; et al. High-resolution retrieval of cloud microphysical properties and surface solar radiation using Himawari-8/AHI next-generation geostationary satellite. Remote Sens. Environ. 2020, 239, 111583. [Google Scholar] [CrossRef] [Scilit]
  22. Yang, J.; Zhang, Z.; Wei, C.; Lu, F.; Guo, Q. Introducing the New Generation of Chinese Geostationary Weather Satellites, Fengyun-4. Bull. Am. Meteorol. Soc. 2017, 98, 1637–1658. [Google Scholar] [CrossRef] [Scilit]
  23. Kotarba, A.Z.; Solecki, M. Uncertainty Assessment of the Vertically-Resolved Cloud Amount for Joint CloudSat–CALIPSO Radar–Lidar Observations. Remote Sens. 2021, 13, 807. [Google Scholar] [CrossRef] [Scilit]
  24. Oreopoulos, L.; Cho, N.; Lee, D. New insights about cloud vertical structure from CloudSat and CALIPSO observations. J. Geophys. Res. Atmos. 2017, 122, 9280–9300. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  25. Okamoto, H.; Sato, K. Cloud Remote Sensing by Active Sensors: New Perspectives from CloudSat, CALIPSO and EarthCARE. In Remote Sensing of Clouds and Precipitation; Springer Remote Sensing/Photogrammetry; Andronache, C., Ed.; Springer: Cham, Switzerland, 2018; pp. 195–214. [Google Scholar]
  26. Hou, A.Y.; Kakar, R.K.; Neeck, S.P.; Azarbarzin, A.A.; Kummerow, C.D.; Iguchi, T. The Global Precipitation Measurement (GPM) mission. Bull. Am. Meteorol. Soc. 2014, 95, 701–722. [Google Scholar] [CrossRef] [Scilit]
  27. Zhang, P.; Gu, S.; Chen, L.; Shang, J.; Lin, M.; Zhu, A.; Yin, H.; Wu, Q.; Shou, Y.; Sun, F.; et al. FY-3G satellite instruments and precipitation products: First report of China’s Fengyun rainfall mission in-orbit. J. Remote Sens. 2023, 3, 0097. [Google Scholar] [CrossRef] [Scilit]
  28. Illingworth, A.; Barker, H.; Beljaars, A.; Ceccaldi, M.; Chepfer, H.; Clerbaux, N.; Cole, J.; Delanoë, J.; Domènech, C.; Donovan, D.P.; et al. The EarthCARE Satellite: The Next Step Forward in Global Measurements of Clouds, Aerosols, Precipitation, and Radiation. Bull. Am. Meteorol. Soc. 2015, 96, 1311–1332. [Google Scholar] [CrossRef] [Scilit]
  29. Wandinger, U.; Haarig, M.; Baars, H.; Donovan, D.; van Zadelhoff, G.-J. Cloud top heights and aerosol layer properties from EarthCARE lidar observations: The A-CTH and A-ALD products. Atmos. Meas. Tech. 2023, 16, 4031–4052. [Google Scholar] [CrossRef] [Scilit]
  30. Mason, S.; Barker, H.; Cole, J.; Docter, N.; Donovan, D.; Hogan, R.; Hünerbein, A.; Kollias, P.; Treserras, B.P.; Qu, Z.; et al. An intercomparison of EarthCARE cloud, aerosol, and precipitation retrieval products. Atmos. Meas. Tech. 2024, 17, 875–892. [Google Scholar] [CrossRef] [Scilit]
  31. Yang, C.; Wang, F.; Lu, Q.; Liu, R.; Yang, X.; Cheng, J.; Cao, Y.; Ni, Z. CNN-based retrieval of 3D cloud structures solely from geostationary satellite imagery. Geophys. Res. Lett. 2026, 53, e2025GL121014. [Google Scholar] [CrossRef] [Scilit]
  32. Liu, A.; Wang, F.; Lu, Q.; Yang, X.; Cao, W.; Yang, C.; Liu, X.; Cao, Y. A multi-task deep learning framework for enhancing cloud-top height retrieval accuracy. Int. J. Digit. Earth 2026, 19, 2605411. [Google Scholar] [CrossRef] [Scilit]
  33. Yang, Y.; Han, W.; Sun, H.; Li, J.; Yan, J.; Gao, Z. Reconstruction of 3D precipitation measurements from FY-3G MWRI-RM imaging and sounding channels. Atmos. Meas. Tech. 2025, 18, 4249–4269. [Google Scholar] [CrossRef] [Scilit]
  34. Yang, Y.; Han, W.; Sun, H.; Xie, H.; Gao, Z. Reconstruction of 3D DPR observations using GMI radiances. Geophys. Res. Lett. 2024, 51, e2023GL106846. [Google Scholar] [CrossRef] [Scilit]
  35. Gstaiger, V.; Köhler, C.; Hochstaffl, P.; Bachmann, M.; Reyes, R.; Holzwarth, S.; Tian, J.; Gege, P.; Paxa, O.; Krauss, T.; et al. Airborne Remote Sensing for Environmental and Disaster Management Applications. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 2025, X-G-2025, 315–322. [Google Scholar] [CrossRef] [Scilit]
  36. Jensen, M.; Petersen, W.; Bansemer, A.; Bharadwaj, N.; Carey, L.; Cecil, D.; Collis, S.M.; Del Genio, A.D.; Dolan, B.; Gerlach, J.; et al. The Midlatitude Continental Convective Clouds Experiment (MC3E). Bull. Am. Meteorol. Soc. 2015, 97, 1667–1686. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  37. Turk, J.F.; Hristova-Veleva, S.; Durden, S.L.; Tanelli, S.; Sy, O.; Emmitt, G.D.; Greco, S.; Zhang, S.Q. Joint analysis of convective structure from the APR-2 precipitation radar and the DAWN Doppler wind lidar during the 2017 Convective Processes Experiment (CPEX). Atmos. Meas. Tech. 2020, 13, 4521–4537. [Google Scholar] [CrossRef] [Scilit]
  38. Stevens, B.; Ament, F.; Bony, S.; Crewell, S.; Ewald, F.; Groß, S.; Hansen, A.; Hirsch, L.; Jacob, M.; Kölling, T.; et al. A High-Altitude Long-Range Aircraft Configured as a Cloud Observatory: The NARVAL Expeditions. Bull. Am. Meteorol. Soc. 2019, 100, 1061–1077. [Google Scholar] [CrossRef] [Scilit]
  39. McMurdie, L.; Heymsfield, G.; Yorks, J.; Braun, S.; Skofronick-Jackson, G.; Rauber, R.M.; Yuter, S.; Colle, B.; McFarquhar, G.M.; Poellot, M.; et al. Chasing Snowstorms: The Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) Campaign. Bull. Am. Meteorol. Soc. 2022, 103, 1234–1250. [Google Scholar] [CrossRef] [Scilit]
  40. Ehrlich, A.; Wendisch, M.; Lüpkes, C.; Buschmann, M.; Bozem, H.; Chechin, D.; Clemen, H.; Dupuy, R.; Eppers, O.; Hartmann, J.; et al. A comprehensive in situ and remote sensing data set from the Arctic CLoud Observations Using airborne measurements during polar Day (ACLOUD) campaign. Earth Syst. Sci. Data. 2019, 11, 1234–1256. [Google Scholar] [CrossRef] [Scilit]
  41. Chang, Y.; Guo, X.; Tang, J.; Lu, G. Aircraft measurement campaign on summer cloud microphysical properties over the Tibetan Plateau. Sci. Rep. 2019, 9, 4912. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  42. Segales, A.R.; Greene, B.R.; Bell, T.M.; Doyle, W.; Martin, J.J.; Pillar-Little, E.A.; Chilson, P.B. The CopterSonde: An insight into the development of a smart unmanned aircraft system for atmospheric boundary layer research. Atmos. Meas. Tech. 2020, 13, 2833–2848. [Google Scholar] [CrossRef] [Scilit]
  43. Xiong, B. Research and Application on Typhoon Detection by UAV. In Proceedings of the 2021 2nd International Conference on Artificial Intelligence in Electronics Engineering (AIEE ’21); Association for Computing Machinery: New York, NY, USA, 2021; pp. 96–102. [Google Scholar]
  44. Inoue, J.; Seko, H.; Sato, K.; Sakai, T. Operational capability of drone-based meteorological profiling in an urban area. J. Geophys. Res. Atmos. 2025, 130, e2024JD041927. [Google Scholar] [CrossRef] [Scilit]
  45. Wu, Z.; Zhang, Y.; Zhang, L.; Hao, X.; Lei, H.; Zheng, H. Validation of GPM Precipitation Products by Comparison with Ground-Based Parsivel Disdrometers over Jianghuai Region. Water 2019, 11, 1260. [Google Scholar] [CrossRef] [Scilit]
  46. Küchler, N.; Kneifel, S.; Löhnert, U.; Kollias, P.; Czekala, H.; Rose, T. A W-Band Radar–Radiometer System for Accurate and Continuous Monitoring of Clouds and Precipitation. J. Atmos. Ocean. Technol. 2017, 34, 2375–2392. [Google Scholar] [CrossRef] [Scilit]
  47. Zhang, W.; Xu, G.; Xi, B.; Ren, J.; Wan, X.; Zhou, L.; Cui, C.; Wu, D. Comparative study of cloud liquid water and rain liquid water obtained from microwave radiometer and micro rain radar observations over central China during the monsoon. J. Geophys. Res. Atmos. 2020, 125, e2020JD032456. [Google Scholar] [CrossRef] [Scilit]
  48. Garcia-Benadi, A.; Bech, J.; Udina, M.; Campistron, B.; Paci, A. Multiple Characteristics of Precipitation Inferred from Wind Profiler Radar Doppler Spectra. Remote Sens. 2022, 14, 5023. [Google Scholar] [CrossRef] [Scilit]
  49. Guo, X.; Guo, J.; Zhang, D.-L.; Yun, Y. Vertical divergence profiles as detected by two wind-profiler mesonets over East China: Implications for nowcasting convective storms. Q. J. R. Meteorol. Soc. 2023, 149, 1629–1649. [Google Scholar] [CrossRef] [Scilit]
  50. Qi, Y.; Fan, S.; Li, B.; Mao, J.; Lin, D. Assimilation of Ground-Based Microwave Radiometer on Heavy Rainfall Forecast in Beijing. Atmosphere 2021, 13, 74. [Google Scholar] [CrossRef] [Scilit]
  51. Kollias, P.; Bharadwaj, N.; Clothiaux, E.; Lamer, K.; Oue, M.; Hardin, J.; Isom, B.; Lindenmaier, I.; Matthews, A.; Luke, E.P.; et al. The ARM Radar Network: At the Leading Edge of Cloud and Precipitation Observations. Bull. Am. Meteorol. Soc. 2020, 101, 1633–1656. [Google Scholar] [CrossRef] [Scilit]
  52. Laj, P.; Myhre, C.; Riffault, V.; Amiridis, V.; Fuchs, H.; Eleftheriadis, K.; Petäjä, T.; Salameh, T.; Kivekäs, N.; Juurola, E.; et al. Aerosol, Clouds and Trace Gases Research Infrastructure (ACTRIS): The European Research Infrastructure Supporting Atmospheric Science. Bull. Am. Meteorol. Soc. 2024, 105, E1098–E1136. [Google Scholar] [CrossRef] [Scilit]
  53. Smith, J.A.; Seo, D.J.; Baeck, M.L.; Hudlow, M.D. An Intercomparison Study of NEXRAD Precipitation Estimates. Water Resour. Res. 1996, 32, 2035–2045. [Google Scholar] [CrossRef] [Scilit]
  54. Min, C.; Chen, S.; Gourley, J.J.; Chen, H.; Huang, C. Coverage of China new generation weather radar network. Adv. Meteorol. 2019, 7, 5789358. [Google Scholar] [CrossRef] [Scilit]
  55. Huuskonen, A.; Saltikoff, E.; Holleman, I. The operational weather radar network in Europe. Bull. Am. Meteorol. Soc. 2014, 95, 897–907. [Google Scholar] [CrossRef] [Scilit]
  56. Asai, K.; Kikuchi, H.; Ushio, T.; Hobara, Y. Validation of X-Band Multiparameter Phased-Array Weather Radar by Comparing Data from Doppler Weather Radar with a Parabolic Dish Antenna. J. Atmos. Ocean. Technol. 2021, 38, 1561–1570. [Google Scholar] [CrossRef] [Scilit]
  57. Walraven, B.; Overeem, A.; Coenders-Gerrits, M.; Hut, R.; van der Valk, L.; Uijlenhoet, R. Relating Rainfall Retrieval Parameters to Network and Environmental Features to Improve Rainfall Estimates from Commercial Microwave Links in the Tropics. J. Hydrometeor. 2024, 25, 1769–1791. [Google Scholar] [CrossRef] [Scilit]
  58. Wu, Z.; Huang, Y.; Zhang, Y.; Zhang, L.; Lei, H.; Zheng, H. Precipitation characteristics of typhoon Lekima (2019) at landfall revealed by joint observations from GPM satellite and S-band radar. Atmos. Res. 2021, 260, 105714. [Google Scholar] [CrossRef] [Scilit]
  59. Liu, C.; Li, X.; Xue, Y.; Lu, W.; Zhang, C. Development and applications of an integrated space-air-ground observation network in natural resource monitoring and supervision. E3S Web Conf. 2024, 520, 04018. [Google Scholar] [CrossRef] [Scilit]
  60. Rosenfeld, D.; Kokhanovsky, A.; Goren, T.; Gryspeerdt, E.; Hasekamp, O.; Jia, H.; Lopatin, A.; Quaas, J.; Pan, Z.; Sourdeval, O. Frontiers in satellite-based estimates of cloud-mediated aerosol forcing. Rev. Geophys. 2023, 61, e2022RG000799. [Google Scholar] [CrossRef] [Scilit]
  61. Zhang, Y.; Wu, Z.; Zhang, L.; Xie, Y.; Huang, Y.; Zheng, H. Preliminary study of land-sea microphysics associated with East Asian Summer Monsoon Rainband and its application to GPM DPR. J. Atmos. Ocean. Technol. 2020, 37, 1231–1249. [Google Scholar] [CrossRef] [Scilit]
  62. Buggee, A.J.; Pilewskie, P. Retrieving vertical profiles of cloud droplet effective radius using multispectral measurements from MODIS: Examples and limitations. Atmos. Meas. Tech. 2025, 18, 5299–5320. [Google Scholar] [CrossRef] [Scilit]
  63. Sun, J.; Li, Y.; Hu, H.; Li, Q.; Ye, C.; Shi, Y.; Chen, Z. Impact of cloud vertical structure perturbations on the retrieval of cloud optical thickness and effective radius from FY4A/AGRI. Atmos. Chem. Phys. 2025, 25, 16347–16361. [Google Scholar] [CrossRef] [Scilit]
  64. Bruno, O.; Hoose, C.; Storelvmo, T.; Coopman, Q.; Stengel, M. Exploring the Cloud Top Phase Partitioning in Different Cloud Types Using Active and Passive Satellite Sensors. Geophys. Res. Lett. 2021, 48, e2020GL089863. [Google Scholar] [CrossRef] [Scilit]
  65. Cesana, G.; Ackerman, A.; Fridlind, A.; Silber, I.; Kelley, M. Snow Reconciles Observed and Simulated Phase Partitioning and Increases Cloud Feedback. Geophys. Res. Lett. 2021, 48, e2021GL094876. [Google Scholar] [CrossRef] [Scilit]
  66. Wang, Z.; Letu, H.; Shang, H.; Bugliaro, L. Technical note: Retrieval of the supercooled liquid fraction in mixed-phase clouds from Himawari-8 observations. Atmos. Chem. Phys. 2024, 24, 7559–7574. [Google Scholar] [CrossRef] [Scilit]
  67. Fu, Y.; Ma, Y.; Zhong, L.; Yang, Y.; Guo, X.; Wang, C.; Xu, X.; Yang, K.; Xu, X.; Liu, L.; et al. Land-surface processes and summer-cloud-precipitation characteristics in the Tibetan Plateau and their effects on downstream weather: A review and perspective. Natl. Sci. Rev. 2020, 7, 500–515. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  68. Peng, C.; Song, C.; Huang, Y.; Ma, X.; Tao, Y. Aircraft Observed Cloud Microphysics Variation Related to a Cloud Trough during Stratiform Cloud Seeding over Central China. J. Meteorol. Res. 2025, 39, 1039–1051. [Google Scholar] [CrossRef] [Scilit]
  69. Wu, Z.; Zhang, Y.; Zhang, L.; Lei, H.; Xie, Y.; Wen, L.; Yang, J. Characteristics of summer season raindrop size distribution in three typical regions of western Pacific. J. Geophys. Res. Atmos. 2019, 124, 4054–4073. [Google Scholar] [CrossRef] [Scilit]
  70. Zheng, H.; Zhang, Y.; Zhang, L.; Lei, H.; Wu, Z. Precipitation Microphysical Processes in the Inner Rainband of Tropical Cyclone Kajiki (2019) over the South China Sea Revealed by Polarimetric Radar. Adv. Atmos. Sci. 2021, 38, 65–80. [Google Scholar] [CrossRef] [Scilit]
  71. Zheng, H.; Wu, Z.; Zhang, L.; Xie, Y.; Lei, H. Improving Radar Rainfall Estimations with Scaled Raindrop Size Spectra in Mei-Yu Frontal Rainstorms. Sensors 2020, 20, 5257. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  72. Seela, B.K.; Janapati, J.; Lin, P.; Lan, C.; Huang, M. Evaluation of GPM DPR Rain Parameters with North Taiwan Disdrometers. J. Hydrometeor. 2023, 25, 47–64. [Google Scholar] [CrossRef] [Scilit]
  73. Wen, L.; Fu, Z.; Fu, P.; Sun, J.; Chen, G. Microphysics of warm season heavy precipitation in 2024 over China: A preliminary investigation based on FY-3G PMR observations. Atmos. Res. 2025, 326, 108293. [Google Scholar] [CrossRef] [Scilit]
  74. Liu, K.; He, J.; Chen, H. Precipitation Retrieval from Fengyun-3D Microwave Humidity and Temperature Sounder Data Using Machine Learning. Remote Sens. 2022, 14, 848. [Google Scholar] [CrossRef] [Scilit]
  75. Liu, Y.; Yang, J.; Zhao, G.; Deng, Y.; Lu, C.; Yin, Y.; Jing, X.; Wang, Y. On the scale of particle clustering induced by inhomogeneous entrainment-mixing in mixed-phase cumulus clouds. Geophys. Res. Lett. 2024, 51, e2023GL107645. [Google Scholar] [CrossRef] [Scilit]
  76. Lu, C.; Liu, Y.; Xu, X.; Gao, S.; Sun, C. Entrainment, Mixing, and Their Microphysical Influences. In Fast Processes in Large-Scale Atmospheric Models; Liu, Y., Kollias, P., Eds.; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2023. [Google Scholar]
  77. Ohneiser, K.; Seifert, P.; Schimmel, W.; Senf, F.; Gaudek, T.; Radenz, M.; Teisseire, A.; Ettrichrätz, V.; Vogl, T.; Maherndl, N.; et al. Impact of seeder-feeder cloud interaction on precipitation formation: A case study based on extensive remote-sensing, in situ and model data. Atmos. Chem. Phys. 2025, 25, 17363–17386. [Google Scholar] [CrossRef] [Scilit]
  78. Wen, L.; Chen, G.; Wang, S.; Nie, J.; Zhao, K. Observed universal continuum morphology of raindrops reveals a concise diagram of heavy precipitation microphysics. Proc. Natl. Acad. Sci. USA 2026, 123, e2525260123. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  79. Li, Q.; Li, H.; Sun, X.; Zhang, Y.; Lyu, W.; Ruan, Z.; Liu, L.; Liu, A.; Zhang, C. A survey of snow growth signatures from tropics to Antarctica using triple-frequency radar observations. Atmos. Chem. Phys. 2026, 26, 1249–1264. [Google Scholar] [CrossRef] [Scilit]
  80. Wen, J.; Zhao, K.; Huang, H.; Zhou, B.; Yang, Z.; Chen, G.; Wang, M.; Wen, L.; Dai, H.; Xu, L.; et al. Evolution of Microphysical Structure of a Subtropical Squall Line Observed by a Polarimetric Radar and a Disdrometer during OPACC in Eastern China. J. Geophys. Res. Atmos. 2017, 122, 8033–8050. [Google Scholar] [CrossRef] [Scilit]
  81. Gultepe, I.; Heymsfield, A.J.; Field, P.R.; Axisa, D. Ice-Phase Precipitation. Meteor. Monogr. 2017, 58, 6.1–6.36. [Google Scholar] [CrossRef] [Scilit]
  82. Takami, K.; Kamamoto, R.; Suzuki, K.; Yamaguchi, K.; Nakakita, E. Relationship between Newly Fallen Snow Density and Polarimetric Parameters Obtained from X-Band Radar Observations along the Sea of Japan Coast in January 2021. J. Atmos. Ocean. Technol. 2024, 41, 935–951. [Google Scholar] [CrossRef] [Scilit]
  83. Takami, K.; Kamamoto, R.; Suzuki, K.; Yamaguchi, K.; Nakakita, E. Relationship between newly fallen snow density and degree of riming estimated by particles’ fall speed in Niigata Prefecture, Japan. Hydrol. Res. Lett. 2022, 16, 87–92. [Google Scholar] [CrossRef] [Scilit]
  84. Zhang, Y.; Zheng, H.; Zhang, L.; Huang, Y.; Liu, X.; Wu, Z. Assessing the effect of riming on snow microphysics: The first observational study in East China. J. Geophys. Res. Atmos. 2021, 126, e2020JD033763. [Google Scholar] [CrossRef] [Scilit]
  85. Wu, Z.; Zhang, Y.; Zhang, L.; Zheng, H.; Huang, X. A Comparison of Convective and Stratiform Precipitation Microphysics of the Record-breaking Typhoon In-Fa (2021). Remote Sens. 2022, 14, 344. [Google Scholar] [CrossRef] [Scilit]
  86. Huang, X.; Wu, Z.; Xie, Y.; Zhang, Y.; Zhang, L.; Zheng, H.; Xiao, W. Precipitation Microphysics of Locally-Originated Typhoons in the South China Sea Based on GPM Satellite Observations. Remote Sens. 2023, 15, 2657. [Google Scholar] [CrossRef] [Scilit]
  87. Deng, L.; Bao, X.; Geresdi, I.; Xue, L.; Huang, W.; Zhao, Y.; Wu, W.; Li, T. Previously neglected effects of strong horizontal winds on raindrop collisions in tropical cyclones. Geophys. Res. Lett. 2025, 52, e2025GL115535. [Google Scholar] [CrossRef] [Scilit]
  88. Wu, Z.; Zhang, Y.; Zhang, L.; Zheng, H. Interaction of cloud dynamics and microphysics during the rapid intensification of super-typhoon Nanmadol (2022) based on multi-satellite observations. Geophys. Res. Lett. 2023, 50, e2023GL104541. [Google Scholar] [CrossRef] [Scilit]
  89. Ou, Y.; Wu, Z.; Xie, Y.; Xiang, Q. Distinct outflow dynamics and cloud properties in rapidly intensifying tropical cyclones. Geophys. Res. Lett. 2025, 52, e2025GL118372. [Google Scholar] [CrossRef] [Scilit]
  90. Zhao, C.; Zhang, Y.; Zheng, D.; Xu, L.; Yao, W. Physical Interpretation and Implications of Convective Impulses in Thunderstorms Based on Lightning and Polarimetric Radar Observations. Atmos. Chem. Phys. 2026, 26, 11091–11110. [Google Scholar] [CrossRef] [Scilit]
  91. Zhao, K.; Xu, X.; Xue, M.; Tan, Z.-M.; Huang, H.; Zhou, A.; Fan, X.; Liu, Q.; Wang, C.; Fang, J.; et al. The Dynamics and Microphysical Characteristics of the Convection Producing the Record-Breaking Hourly Precipitation on 20 July 2021 in Zhengzhou, China. Remote Sens. 2023, 15, 4511. [Google Scholar] [CrossRef] [Scilit]
  92. Wu, B.; Du, S.; Li, W.; Shen, Y.; Luo, L.; Li, Y.; Wei, M.; Wang, D.; Xi, L. The Kinematic and Microphysical Characteristics of Extremely Heavy Rainfall in Zhengzhou City on 20 July 2021 Observed with Dual-Polarization Radars and Disdrometers. Remote Sens. 2023, 15, 5688. [Google Scholar] [CrossRef] [Scilit]
  93. Li, H.; Yin, J.; Kumjian, M. ZDR Backwards Arc: Evidence of Multi-Directional Size Sorting in the Storm Producing 201.9 Mm Hourly Rainfall. Geophys. Res. Lett. 2024, 51, e2024GL109192. [Google Scholar] [CrossRef] [Scilit]
  94. Cui, L.; Li, H.; Su, A.; Zhang, Y.; Lyu, X.; Xi, L.; Zhang, Y. Raindrop Size Distributions in the Zhengzhou Extreme Rainfall Event on 20 July 2021: Temporal–Spatial Variability and Implications for Radar QPE. J. Meteorol. Res. 2024, 38, 489–503. [Google Scholar] [CrossRef] [Scilit]
  95. Yin, J.; Xia, R.; Li, M.; Li, H.; Liang, X. The Extreme Short-term Rainfall Rate as Caused by a Convective Storm under a Beneficial Dynamic Pattern. Q. J. R. Meteorol. Soc. 2025, 151, e4968. [Google Scholar] [CrossRef] [Scilit]
  96. Wu, Z.; Zhang, Y.; Xie, Y.; Zhang, L.; Zheng, H. Radiance-Based Assessment of Bulk Microphysics Models with Seven Hydrometeor Species in Forecasting Super-typhoon Lekima (2019) near Landfall. Atmos. Res. 2022, 273, 106173. [Google Scholar] [CrossRef] [Scilit]
  97. Zhang, Y.; Wu, Z.; Zhang, L.; Zheng, H. A Comparison of Spectral Bin Microphysics versus Bulk Parameterization in Forecasting Typhoon In-Fa (2021) before, during, and after Its Landfall. Remote Sens. 2022, 14, 2169. [Google Scholar] [CrossRef] [Scilit]
  98. Wu, Z.; Zhang, Y.; Zhang, L.; Zheng, H. Improving the WRF Forecast of Landfalling Tropical Cyclones over the Asia-Pacific Region by Constraining the Cloud Microphysics Model with GPM Observations. Geophys. Res. Lett. 2022, 49, e2022GL100053. [Google Scholar] [CrossRef] [Scilit]
  99. Zheng, H.; Zhang, Y.; Chen, Y.; Ding, D.; Wu, Z.; Huang, M.; He, H.; Ma, X.; Zhou, Z. Microphysical and dynamic evolution of convection observed by polarimetric radar under the influence of cloud seeding. Atmos. Res. 2023, 297, 107110. [Google Scholar] [CrossRef] [Scilit]
  100. Zhao, C.; Yang, Y.; Chi, Y.; Sun, Y.; Zhao, X.; Letu, H.; Xia, Y. Recent progress in cloud physics and associated radiative effects in China from 2016 to 2022. Atmos. Res. 2023, 281, 106899. [Google Scholar]
  101. Kaur, I.; Eriksson, P.; Barlakas, V.; Pfreundschuh, S.; Fox, S. Fast Radiative Transfer Approximating Ice Hydrometeor Orientation and Its Implication on IWP Retrievals. Remote Sens. 2022, 14, 1594. [Google Scholar] [CrossRef] [Scilit]
  102. Vahedizade, S.; Ebtehaj, A.; Tamang, S.; You, Y.; Panegrossi, G.; Ringerud, S.; Turk, F.J. On the effects of cloud water content on passive microwave snowfall retrievals. Remote Sens. Environ. 2022, 280, 113187. [Google Scholar] [CrossRef] [Scilit]
  103. Seto, S.; Iguchi, T.; Shimozuma, T.; Hayashi, S. NUBF correction methods for the GPM/DPR level-2 algorithms. In IEEE International Geoscience and Remote Sensing Symposium; IEEE: New York, NY, USA, 2015; pp. 2612–2614. [Google Scholar]
  104. Sasikumar, S.; Battaglia, A.; Puigdomènech Treserras, B.; Kollias, P. The estimation of path integrated attenuation for the EarthCARE cloud profiling radar. Atmos. Meas. Tech. 2026, 19, 2313–2328. [Google Scholar] [CrossRef] [Scilit]
  105. Ohno, T.; Matsui, M.; Itokawa, K.; Tokuyasu, T.; Ito, K.; Shige, S.; Takemi, T. Cloud Attenuation Estimation Based on Cloud Particle Sensor Sonde Observations for Next-Generation Satellite Communications. Sci. Rep. 2026. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  106. Battaglia, A.; Mroz, K.; Watters, D.; Ardhuin, F. GPM-Derived Climatology of Attenuation Due to Clouds and Precipitation at Ka-Band. IEEE Trans. Geosci. Remote Sens. 2019, 58, 1812–1820. [Google Scholar] [CrossRef] [Scilit]
  107. Waitz, F.; Schnaiter, M.; Leisner, T.; Järvinen, E. In situ observation of riming in mixed-phase clouds using the PHIPS probe. Atmos. Chem. Phys. 2022, 22, 7087–7103. [Google Scholar] [CrossRef] [Scilit]
  108. Korolev, A.; McFarquhar, G.; Field, P.R.; Franklin, C.; Lawson, P.; Wang, Z.; Williams, E.; Abel, S.J.; Axisa, D.; Borrmann, S.; et al. Mixed-Phase Clouds: Progress and Challenges. Meteor. Monogr. 2017, 58, 5.1–5.50. [Google Scholar] [CrossRef] [Scilit]
  109. Li, H.; Moisseev, D.; Luo, Y.; Liu, L.; Ruan, Z.; Cui, L.; Bao, X. Assessing Specific Differential Phase (KDP)-Based Quantitative Precipitation Estimation for the Record- Breaking Rainfall over Zhengzhou City on 20 July 2021. Hydrol. Earth Syst. Sci. 2023, 27, 1033–1046. [Google Scholar] [CrossRef] [Scilit]
  110. Pan, R.; Liu, X.; Shi, J. Measurement of solid precipitation and blowing snow: Current status and challenges. Adv. Polar Sci. 2024, 35, 289–302. [Google Scholar]
  111. Fang, R.; Ding, J.; Gao, W.; Liang, X.; Chen, Z.; Zhao, C.; Dai, H.; Liu, L. IMPMCT: A dataset of Integrated Multi-source Polar Mesoscale Cyclone Tracks in the Nordic Seas. Earth Syst. Sci. Data. 2025, 17, 6049–6069. [Google Scholar] [CrossRef] [Scilit]
  112. Yang, C.; Li, H.; Zhu, R.; Wang, Y.; Zhang, F.; Gu, M.; Jiang, G.-M.; Zhang, R.; Tang, X. Snow or rain? hybrid AI deciphers surface precipitation phase from satellite observations. Nat. Commun. 2026, 17, 2813. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  113. Chen, H.; Chandrasekar, V.; Cifelli, R.; Xie, P. A Machine Learning System for Precipitation Estimation Using Satellite and Ground Radar Network Observations. IEEE Trans. Geosci. Remote Sens. 2020, 58, 982–994. [Google Scholar] [CrossRef]
  114. Nourani, V.; Tosan, M.; Huang, J.J.; Gebremichael, M.; Kantoush, S.A.; Dastourani, M. Advances in multi-source data fusion for precipitation estimation: Remote sensing and machine learning perspectives. Earth-Sci. Rev. 2025, 270, 105253. [Google Scholar] [CrossRef] [Scilit]
  115. Gorooh, V.; Kalia, S.; Nguyen, P.; Hsu, K.; Sorooshian, S.; Ganguly, S.; Nemani, R.R. Deep Neural Network Cloud-Type Classification (DeepCTC) Model and Its Application in Evaluating PERSIANN-CCS. Remote Sens. 2020, 12, 316. [Google Scholar] [CrossRef] [Scilit]
  116. Zhao, S.; Wang, F.; Huang, X.; Yang, X.; Jiang, N.; Peng, J. Mamba-UNet: Dual-Branch Mamba Fusion U-Net With Multiscale Spatio-Temporal Attention for Precipitation Nowcasting. IEEE Trans. Ind. Inf. 2025, 21, 4466–4475. [Google Scholar] [CrossRef] [Scilit]
  117. Li, M.; Huang, X.; Wang, F.; Yang, X.; Peng, J.; Ban, Y. ADNM-UNet: An Asymmetric Dual-Branch Noncausal Mamba U-Net With Multiscale Attention Enhancement for Cloud Mask Nowcasting. IEEE Trans. Geosci. Remote Sens. 2025, 63, 5110215. [Google Scholar] [CrossRef] [Scilit]
  118. Zhang, M.; Liu, X. Three-Dimensional Cloud Prediction Based on Hybrid Quantum Neural Networks. In Proceedings of the 5th International Conference on Electronic Information Engineering and Computer Technology (EIECT), Jiangmen, China, 24–26 October 2025; pp. 624–627. [Google Scholar] [CrossRef] [Scilit]
  119. Gettelman, A.; Geer, A.J.; Forbes, R.M.; Carmichael, G.R.; Feingold, G.; Posselt, D.J.; Stephens, G.L.; Heever, S.C.v.D.; Varble, A.C.; Zuidema, P. The future of Earth system prediction: Advances in model-data fusion. Sci. Adv. 2022, 8, eabn3488. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  120. Sun, S.; Nai, C.; Pan, B.; Li, W.; Li, L.; Li, X.; Foufoula-Georgiou, E.; Lin, Y. Fusion of multi-source precipitation records via coordinate-based generative models. Nat. Commun. 2025, 17, 1227. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  121. Munro, R.; Bojkov, B.; Fadrique, F.; Counet, P.; Lindstrot, R.; Köhler, P.; Hao, N.; Diekmann, C.; Fougnie, B.; Navarro, M.V.; et al. EUMETSAT Polar System–Second Generation: Continuation of Observations and Advancing Applications from Low-Earth Orbit. Bull. Am. Meteorol. Soc. 2026, 107, E628–E664. [Google Scholar] [CrossRef] [Scilit]
  122. Werdell, J.; Cairns, B.; Caplan, S.; Cetinić, I.; Foley, S.; Franz, B.; Gao, M.; Fasnacht, Z.T.; Huemmrich, K.F.; Ibrahim, A.; et al. Advancing Earth System Science With the NASA Plankton, Aerosol, Cloud, Ocean Ecosystem (PACE) Satellite Mission. Glob. Change Biol. 2026, 32, e70869. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  123. Gao, L.; Zhang, Q.; Wang, P.; Wang, Q.; Li, J.; Bi, Y.; Zhang, X. A Review on the Chinese Fengyun Series Meteorological Satellites for Atmospheric Composition Monitoring. Bull. Am. Meteorol. Soc. 2026, 107, E1092–E1108. [Google Scholar] [CrossRef] [Scilit]
  124. Ohara, K.; Masunaga, H. Evaluation of ice hydrometeor retrieval using multi-band radar and millimeter-wave radiometer measurements from the IMPACTS campaign. Atmos. Meas. Tech. 2026, 19, 4099–4119. [Google Scholar] [CrossRef] [Scilit]
  125. Zhang, P.; Liu, X.; Pu, K. Improving Quantitative Precipitation Estimation Using Adaptive Z-R Relationship Adjustment: Merging Weather Radar With Commercial Microwave Links. IEEE Trans. Geosci. Remote Sens. 2025, 63, 1–10. [Google Scholar] [CrossRef] [Scilit]
  126. Rubin, Y.; Rostkier-Edelstein, D.; Chwala, C.; Alpert, P. Challenges in Diurnal Humidity Analysis from Cellular Microwave Links (CML) over Germany. Remote Sens. 2022, 14, 2353. [Google Scholar] [CrossRef] [Scilit]
  127. Zhao, K.; Huang, H.; Lu, Y.; Chen, H.; Wu, C.; Zhao, G.; Zhu, Y.; Tan, Z.-M.; Zhang, Y.; Fu, P.; et al. Operational Phased Array Radar Network for Natural Hazard Monitoring and Warnings in Urban Environments over the Greater Bay Area, China. Bull. Am. Meteorol. Soc. 2024, 105, E2152–E2174. [Google Scholar] [CrossRef] [Scilit]
  128. Shi, J.; Liu, X.; Liu, L.; Liu, L.; Wang, P. An introduction of the Three-Dimensional Precipitation Particle Imager (3D-PPI). Atmos. Meas. Tech. 2025, 18, 2261–2278. [Google Scholar] [CrossRef] [Scilit]
Remotesensing 18 02798 g001
Figure 1. Complementarity and integration of space–air–ground observations for clouds and precipitation physics across scales.
Figure 1. Complementarity and integration of space–air–ground observations for clouds and precipitation physics across scales.
Remotesensing 18 02798 g001
Remotesensing 18 02798 g002
Figure 2. Conceptual framework connecting remote sensing observations, retrieval strategies, and cloud-precipitation physical processes (black dashed box), highlighting current challenges and emerging frontiers (gray dashed box). Challenges across platforms, algorithms, and applications stimulate technological innovation (red arrows), which in turn addresses these challenges and drives future research and application development (purple arrows).
Figure 2. Conceptual framework connecting remote sensing observations, retrieval strategies, and cloud-precipitation physical processes (black dashed box), highlighting current challenges and emerging frontiers (gray dashed box). Challenges across platforms, algorithms, and applications stimulate technological innovation (red arrows), which in turn addresses these challenges and drives future research and application development (purple arrows).
Remotesensing 18 02798 g002
Table 1. Overview of major remote sensing platforms and instruments for clouds and precipitation physics, including their main strengths and key limitations.
Table 1. Overview of major remote sensing platforms and instruments for clouds and precipitation physics, including their main strengths and key limitations.
Platform and Sensing ModeRepresentative SystemsObservation PrincipleMain StrengthsKey Limitations
Satellite passive sensing(Polar-orbiting)
MODIS, VIIRS, ATMS		Measure optical/infrared/microwave radiation to retrieve cloud and precipitation propertiesGlobal coverage, multispectral observations, mature climate recordsLimited temporal sampling, cloud heterogeneity, surface-background effects
(Geostationary)
FCI, ABI, AHI, AGRI		Continuously observe optical/infrared radiation from fixed orbital positions for monitoring cloud-top propertiesHigh temporal resolution, continuous monitoringLimited vertical information, coarse spatial resolution, viewing-angle effects
Satellite active sensingCloudSat, CALIPSO, GPM DPR, FY-3G PMR, EarthCAREEmit radar/lidar signals and analyze returned backscatter to characterize cloud and precipitation vertical structuresVertical structure, phase discrimination, three-dimensional precipitationSampling gaps, attenuation, revisit constraints
Airborne remote sensingW/Ka cloud radar, lidar, microwave radiometer, polarimeterCombine active radar/lidar observations and passive microwave/optical measurements for targeted high-resolution samplingTargeted high-resolution observations and satellite underflight validationShort campaigns and limited spatial representativeness
Airborne in-situCloud droplet probe, cloud/precipitation imaging probe, two-dimensional stereo probe, condensation particle counter, continuous-flow diffusion chamberDirectly sample cloud particles, aerosols, and hydrometeors to obtain microphysical propertiesDirect particle-scale truth for retrieval and model evaluationSafety and sampling constraints in severe weather
Ground-based profilingCloud radar, lidar, micro rain radar, microwave radiometer, wind profilerMeasure atmospheric columns using active and passive techniques to obtain vertical profiles of clouds, precipitation, thermodynamic state, and dynamicsContinuous vertical evolution and benchmark datasetsLocal coverage and terrain/blockage issues
Ground precipitation sensingPolarimetric radar, disdrometer, precipitation gauge, commercial microwave linkDetect hydrometeor scattering, particle size, and attenuation characteristics to quantify precipitation properties near the surfaceHydrometeor classification and near-surface precipitation characterizationCoverage gaps over ocean and remote regions, calibration needs
Table 2. Summary of key cloud and precipitation variables, corresponding sensors, strengths, and limitations.
Table 2. Summary of key cloud and precipitation variables, corresponding sensors, strengths, and limitations.
VariableSensorsStrengthLimitationBest Use
COT/CERPolar-orbiting and geostationaryGlobal coverageCloud heterogeneityClimate studies
LWC/IWCRadar and radiometerVertical informationRetrieval ambiguityMixed-phase
PSD (Dm/Nw)DPR and polarimetric radarMicrophysicsNear-surface biasHeavy rain
Precipitation phaseLidar and radarHigh accuracyAttenuationIce/liquid
Table 3. Summary of key cloud-precipitation physical processes, mechanisms, and observational characteristics.
Table 3. Summary of key cloud-precipitation physical processes, mechanisms, and observational characteristics.
Process CategoryKey ProcessesDominant MechanismsObservational SignaturesMain Observational InstrumentsPhysical Significance
Cloud dynamics and structureEntrainment mixingTurbulent mixing with environmental air, cloud-top radiative coolingBroadening of particle size spectra, cloud-top variabilityCloud radar, lidarRegulates cloud evolution and precipitation initiation
Phase transitionLiquid–ice phase conversionSupercooled liquid water, ice nucleation processesMixed-phase cloud layers, coexistence of liquid and iceRadar, lidarDetermines precipitation pathways and efficiency
Warm-rain processesCollision-coalescenceDroplet collision efficiency and gravitational collectionRapid increase in droplet size, onset, and intensification of rainfallRadar, microwave radiometerDominant mechanism in maritime and shallow convection
Cold and mixed-phase processesDeposition, aggregation, rimingIce crystal growth, particle collision, supercooled water accretionFormation of snow, graupel, and ice particlesDual-polarization radar, airborne in-situ probesKey processes for snowfall and intense precipitation
Precipitation initiationCondensation-to-coalescence transitionFormation of critical droplet size thresholdChange in precipitation onset height, reflectivity increaseCloud radar, GPM DPR, FY-3G PMRIndicates the onset and timing of precipitation
Hydrometeor growthParticle size distribution evolutionMicrophysical growth and breakup processesVariations in Dm and Nw parametersDual-frequency radar, polarimetric radarEssential for quantitative precipitation estimation
Near-surface processesEvaporation, breakup, meltingThermodynamic and dynamical interactions near the surfaceReduction in droplet size, phase transition of hydrometeorsDisdrometer, precipitation gauge, weather radarModifies surface precipitation intensity and type
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Wu, Z.; Wen, L.; Zeng, Y.; Gultepe, I. Recent Progress in Remote Sensing of Clouds and Precipitation Physics: Platforms, Applications, and Emerging Frontiers. Remote Sens. 2026, 18, 2798. https://doi.org/10.3390/rs18162798

AMA Style

Wu Z, Wen L, Zeng Y, Gultepe I. Recent Progress in Remote Sensing of Clouds and Precipitation Physics: Platforms, Applications, and Emerging Frontiers. Remote Sensing. 2026; 18(16):2798. https://doi.org/10.3390/rs18162798

Chicago/Turabian Style

Wu, Zuhang, Long Wen, Yong Zeng, and Ismail Gultepe. 2026. "Recent Progress in Remote Sensing of Clouds and Precipitation Physics: Platforms, Applications, and Emerging Frontiers" Remote Sensing 18, no. 16: 2798. https://doi.org/10.3390/rs18162798

APA Style

Wu, Z., Wen, L., Zeng, Y., & Gultepe, I. (2026). Recent Progress in Remote Sensing of Clouds and Precipitation Physics: Platforms, Applications, and Emerging Frontiers. Remote Sensing, 18(16), 2798. https://doi.org/10.3390/rs18162798

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop