First insights into deep convection by the Doppler velocity measurements of the EarthCARE's Cloud Profiling Radar

First insights into deep convection by the Doppler velocity measurements of the EarthCARE’s Cloud Profiling Radar Aida Galfione1, Alessandro Battaglia1,2, Bernat Puigdomènech Treserras3, and Pavlos Kollias3,4 1Department of Environment, Land and Infrastructure Engineering, Politecnico di Torino, 10129 Turin, Italy 2Earth Observation Science Group, Department of Physics and Astronomy, University of Leicester, Leicester LE1 7RH, UK 3Department of Atmospheric and Oceanic Sciences, McGill University, Montreal, H3A 0B9, QC Canada 4School of Marine and Atmospheric Science, Stony Brook University, NY 11790, NY USA Correspondence: Aida Galfione ([email protected]) Abstract. Convective updrafts and downdrafts play a vital role in Earth’s energy and water cycles by modulating vertical energy and moisture transport and shaping precipitation patterns. Despite their importance, the characteristics of convective motions and their relationship to the near-storm environment remain poorly constrained by observations. The payload of the recently launched EarthCARE satellite mission includes a 94-GHz Cloud Profiling Radar (CPR) with5 Doppler capability. In this study, we present first-light CPR Doppler velocity observations in deep convective clouds. These early examples offer a first glimpse into the dynamic nature of cloud systems. The narrow footprint of the CPR helps reduce the impact of multiple scattering and non-uniform beam filling (NUBF) on the Doppler velocity measurements. However, the instrument’s low Nyquist velocity presents a significant challenge for recovering the true Doppler velocity profiles in deep convective systems.10 The CPR Doppler velocity observations are expected to challenge traditional methodologies for identifying deep convective cores, which typically rely on reflectivity-based thresholds. We showcase examples that demonstrate the synergy between CPR Doppler velocity measurements and geostationary satellite observations, illustrating how their combined use can help capture the evolution of the convective lifecycle. These results align with EarthCARE’s broader mission objectives and highlight the potential of spaceborne Doppler radar to15 significantly advance our understanding of cloud dynamics and convection in the climate system. 1 Introduction Deep convective clouds are responsible for the vertical transport of air and water, one of the most influential yet poorly constrained by measurements atmospheric process. Deep convection is crucial in balancing the Earth’s heat budget and influencing large-scale weather patterns, including cloud formation and the development of storms and extreme weather (Hartmann et al.20 1984). Deep convective events typically occur in tropical regions, but they affect the global atmospheric circulation beyond the tropics via anvil detrainment processes and latent heat release via precipitation (Gasparini et al. 2021; Hartmann et al. 2018). 1 https://doi.org/10.5194/egusphere-2025-1914 Preprint. Discussion started: 15 May 2025 c Author(s) 2025. CC BY 4.0 License. A number of microphysical processes are active during convective initiation and development that are not yet well understood or properly implemented in models (Prein et al. 2015; Arakawa 2004; Bony et al. 2015). Despite the importance of deep convection, several aspects of deep convective clouds remain challenging to represent in high-25 resolution models and even observations (Fridlind et al. 2017; Ladino et al. 2017). Models also struggle to accurately represent convective updrafts, leaving significant observational gaps (Varble et al. 2014). Surface and airborne radar observations have provided valuable insight into the structure and magnitude of convective updrafts, but the observational record is very sparse and mostly available over land (Giangrande et al. 2013; Wang et al. 2020; Oue et al. 2019; J. Yang et al. 2016; Jeyaratnam et al. 2021; North et al. 2017). On the other hand, satellite observations can provide global coverage and sufficient sampling of deep30 convection and associated clouds and precipitation (Lee et al. 2021). In particular, the 3-D structure of deep convective clouds has been extensively studied using observations from spaceborne radars. The Tropical Rainfall Measuring Mission (TRMM), developed by the National Aeronautics and Space Administration (NASA) and the National Space Development Agency of Japan (NASDA), introduced the first spaceborne radar in space, a 13.8 GHz Precipitation Radar (PR) (Kummerow et al. 1998; Kummerow et al. 2000). The TRMM PR was operational from35 1997 to 2015 and advanced our understanding of tropical convection and associated rainfall (Yokoyama et al. 2014; Xu et al. 2012). Studies using the TRMM PR data analyzed convective system structures, including diurnal cycles and vertical profiles (Hamada et al. 2015). TRMM’s success led to the Global Precipitation Measurement (GPM) mission launched in 2014 by NASA and the Japan Aerospace Exploration Agency (JAXA) which enhances TRMM’s capabilities with improved resolution and higher latitude coverage (Skofronick-Jackson et al. 2017). The GPM mission features a Dual-frequency Precipitation Radar40 (DPR) that operates at Ku (35.5 GHz) and Ka (13.6 GHz) bands, providing 3D precipitation structures (Skofronick-Jackson et al. 2018). Studies using GPM PR data show deep convection reaching the tropopause predominantly over land, consistent with TRMM findings (Liu et al. 2020; Battaglia et al. 2020; Liu et al. 2016). Ni et al. 2019 analyzed ice microphysical properties, revealing larger ice particles and higher ice water content in land-based deep convective cores. The poor sensitivity of the PR and DPR limited their ability to capture the 3D structure of the upper-level tropospheric cloud structures.45 The CloudSat-CALIPSO mission (Stephens et al. 2002), part of NASA’s A-Train since 2004, provided detailed cloud vertical structures. Its Cloud Profiling Radar (CPR) with 240 m vertical resolution captured convective cloud features, aiding studies on convective cores and updrafts (Takahashi et al. 2017a). Findings indicate stronger convective cores and lower entrainment rates over land, enabling higher-altitude particle transport. However, CloudSat’s narrow along-track sampling (1.4 km cross-track) limits representation of spatially heterogeneous deep convective cores (DCCs). To mitigate biases, CloudSat data have been50 integrated with passive sensors, such as MODIS cloud top temperature, for improved convective characterization (Luo et al. 2008; Luo et al. 2010; Luo et al. 2014; K. Yang et al. 2023). Finally, in May 2024, the Earth, Cloud, Aerosol and Radiation Explorer (EarthCARE, Illingworth et al. 2015), a joint European Space Agency (ESA) and JAXA mission was successfully launched. The EarthCARE mission aims to improve cloud-aerosol-radiation interaction studies and enhance numerical weather prediction (NWP) models and climate simulations.55 EarthCARE carries a 94-GHz Doppler Cloud Profiling Radar (CPR), High-Spectral Resolution Lidar (ATLID), Multi-Spectral Imager (MSI), and Broad-Band Radiometer (BBR). Launched after CloudSat-CALIPSO ended operations in 2023, EarthCARE 2 https://doi.org/10.5194/egusphere-2025-1914 Preprint. Discussion started: 15 May 2025 c Author(s) 2025. CC BY 4.0 License. benefits from improved radar sensitivity due to its lower orbit and having all instruments on the same platform (Illingworth et al. 2015; Wehr et al. 2023). Most importantly, the EC mission features the first spaceborne radar with Doppler capability (Kollias et al. 2018b; Kollias et al. 2014a; Kollias et al. 2022a). The availability of Doppler measurements from space will60 offer a unique opportunity for the collection of a global dataset of vertical motions in clouds and precipitation. This global data set is expected to improve our understanding of convective motions in clouds and help evaluate current parameterizations of convective mass flux in cloud resolution models (Manabe et al. 1964; Tiedtke 1989; Bechtold et al. 2001). Here, a first assessment of the performance of the EC CPR Doppler velocity measurements in deep convection is presented. The main objectives of this study are to describe and interpret convective cores as observed by the EC-CPR, leveraging joint65 Doppler velocity and reflectivity measurements, and to compare these observations with geostationary data. For the first time, Doppler velocity from a spaceborne radar is used to identify and characterize convective cores, providing insights into their internal dynamics and updraft structures (Kollias et al. 2023). Coincident MSI observations are compared with geostationary MSG (Meteosat Second Generation) imagery to assess the capability of passive sensors in detecting convection and tracking its evolution.70 2 CPR Doppler velocity observations in deep convection One of the most exciting new measurement capabilities of the EarthCARE mission is the CPR Doppler velocity measurements. Several factors are expected to impact the quality of the CPR Doppler velocity measurements (Tanelli et al. 2002; Tanelli et al. 2005; Kollias et al. 2014b; Kollias et al. 2018a; Kollias et al. 2022b). The EarthCARE satellite speed of 7.6 ms−1introduces significant broadening (decorrelation) of the CPR phase measurements that results in significant uncertainty in the Doppler75 velocity estimates (Kollias et al. 2014b; Kollias et al. 2022b). Antenna mispointing is another source of uncertainty (Tanelli et al. 2005; Battaglia et al. 2014; Puigdomènech Treserras et al. 2025). In deep convection, additional factors such as attenuation, multiple scattering (Battaglia et al. 2008; Battaglia et al. 2010; Battaglia et al. 2011c), non-uniform beam filling (Tanelli et al. 2002; Kollias et al. 2022b), and aliasing (Sy et al. 2014) can have a significant impact on the observed Doppler velocities and introduce considerable uncertainty and biases.80 An example of CPR observations of a deep convective system is shown in Fig. 1. The CPR observations were collected on September 18, 2024, over Western Africa on a descending (daytime) orbit. Here, CPR Level 2a (L2a) C-PRO data products are used (Kollias et al. 2023). These products are derived from the CPR Level 1b data plus auxiliary meteorological data. The L2a C-PRO data product was released available to the research community on March 2025 (Eisinger et al. 2023). The CPR reflective image (Fig. 1a) illustrates the vertical structure of a wide deep precipitating system. CloudSat-based studies of deep85 convection mainly use the reflectivity profile features near cloud top to identify deep convective cores (DCC, Takahashi et al. 2012; Luo et al. 2014; Takahashi et al. 2017b; Stephens et al. 2024). The underlying reasoning is that the overshooting of radar reflectivity is an indicator of the larger-size particles pushed higher up; this is only possible with the presence of strong rising updrafts. Three criteria are commonly adopted for convection identification (Takahashi et al. 2014): –CPR cloud mask (2B-GEOPROF product) greater than 20;90 3 https://doi.org/10.5194/egusphere-2025-1914 Preprint. Discussion started: 15 May 2025 c Author(s) 2025. CC BY 4.0 License. Figure 1. (a) CPR reflectivity during a large-scale, deep precipitating system with embedded convection observed on September 19, 2024 over the Tropical Western Pacific (Frame 1760E). The horizontal line indicates the 10 km height, and the blue circles indicate the maximum height where a dBZ value above +10 dBZ is observed. (b) the CPR Doppler velocity measurements after a 4-km along-track integration (Kollias et al. 2023). Positive Doppler velocities indicate hydrometors’ movement towards the ground. –A continuous radar echo from below 2 to above 10 km, thus a thick cloud deck; –The 10 dBZ echo top height which is indicative of the level where large size particles are lofted by strong convection (Luo et al. 2008) above 10 km. In Fig. 1a the 10 dBZ echo top height is very close to the 10 km height for a significant part of the deep precipitating system. In two areas (775-790 km and 950-975 km along track), the 10 dBZ echo top height is well above the 10 km height and closer95 4 https://doi.org/10.5194/egusphere-2025-1914 Preprint. Discussion started: 15 May 2025 c Author(s) 2025. CC BY 4.0 License. to the cloud top height. Luo et al. 2014 introduced a fourth criterion for detecting DCCs, which requires that the 10 dBZ echo top height be within 2 km of the cloud top height determined by the CPR. The CPR Doppler velocity measurements for the same event can assist into evaluating these different methodologies for identifying DCCs. Figure 1b shows the CPR Doppler velocity averaged over a 4-km along-track distance. The CPR Doppler velocity measurements are shown only in areas where there CPR reflectivity exceeds -21 dBZ. The native CPR along track100 resolution is 500 m, thus, a total of nine CPR Doppler velocity estimates (their respective real and imaginary parts of the lag-1 pulse pair estimator) have been averaged (Kollias et al. 2023). The averaging of the pulse-pair Doppler velocity estimator lag-1 real and imaginary parts is immune to velocity folding. Before the along track averaging, the CPR Doppler velocities have been corrected for antenna mispointing (Puigdomènech Treserras et al. 2025) and non-uniform beam filling (NUBF) Doppler velocity biases (Kollias et al. 2014b; Sy et al. 2014).105 The nadir-pointing CPR Doppler velocity VDrepresents the sum of the vertical air motion WAIR and the reflectivityweighted Doppler sedimentation velocity of the hydrometeors VD T: VD=WAIR +VD T.(1) The VD Tterm can only take positive values (downward motion) while the WAIR term can take both positive (downdraft) and negative (updraft) values. The majority of the observed VDin Fig. 1b are positive. This implies that the VD Tmagnitude is higher110 than that of the embedded WAIR updrafts. This suggests the presence of negligible vertical air motions (|WAIR|<2 ms−1). A typical example profile of the CPR Doppler velocity and corresponding radar reflectivity is stratiform precipitation conditions is shown in Fig. 2. The most pronounced VDfeature is its melting layer signature just below 5 km height that indicates the phase change from the slowly falling solid ice/snow particles to the fast falling liquid raindrops around the 0◦C isotherm (Fig. 2a). The 1-km CPR-averaged Doppler velocity profiles exhibit the same trend but exhibit considerable fluctuations (Kollias115 et al. 2014b). The noisiness of the CPR 1-km averaged Doppler velocities makes the estimation of the hydrometeors’ size and/or density at the 1-km resolution challenging (Kollias et al. 2022b; Mroz et al. 2023). The melting layer signature is also evident in the CPR reflectivity profile with a pronounced increase around the 0 ◦C isotherm (Fig. 2b). The ice-to-rain Doppler velocity transition is a well-known feature of the Doppler velocity in cold-rain systems, routinely observed by ground-based and airborne Doppler radars (among others, Fabry et al. 1995; Heymsfield et al. 2010), but for the first time with EC it is120 possible to see it from space. The CPR short wavelength (λ= 3.2mm) and Pulse Repetition Frequency (PRF) determine the CPR Nyquist velocity (VN) folding (VN=λPRF/4, yellow lines in Fig. 2a). The VNis the maximum unambiguous velocity that can be detected by the CPR without aliasing (or velocity folding). If the VDexceeds VNthen folding occurs. During stratiform conditions, in the ice layer, velocity folding is rare even for the 1-km CPR Doppler velocity estimates (Fig. 2a). Below the melting layer, VD T 125 can reach values up to 6.5 ms−1(Kollias et al. 2022c). Here, velocity folding can occur especially in the 1-km CPR Doppler velocity estimates, which are altogether noisier. In Fig. 2a the 1-km Doppler velocity estimates outside the VNboundaries have already been corrected for velocity folding. The assumption used for the unfolding is that negative Doppler velocities below 5 https://doi.org/10.5194/egusphere-2025-1914 Preprint. Discussion started: 15 May 2025 c Author(s) 2025. CC BY 4.0 License. Figure 2. (a) the CPR Doppler velocity profiles at along track distance of 875 km. The 4-km CPR Doppler velocity estimate is shown in green circles and the 1-km Doppler velocity estimates within a 2 km distance from 875 km are shown in gray lines. The yellow vertical lines indicate the CPR Nyquist velocity and the horizontal dashed line indicates the melting layer height. (b) the corresponding CPR reflectivity at along track distance of 875 km. the melting layer in a stratiform precipitation profile are the results of VDexceeding +VN. Subsequently, all negative VDvalues below the 0°C isotherm are unfolded by adding 2VNto them.130 The interpretation of the CPR Doppler velocity profile in deep stratiform layers serves as a baseline for understanding convective updrafts. In Fig. 1b, updrafts are depicted as regions with negative (upward) 4-km-averaged VDestimates in cold temperatures (Fig. 1b). Not including the along-track interval 950-975 km, the clusters of negative VDare located near the cloud top. Since ice particles are smaller at colder temperatures, it is plausible that near cold cloud tops, weak gravity waves and updrafts contribute to an overall negative (upward) CPR Doppler velocity signal. The estimation of air vertical velocity,135 WAIR, requires knowledge of the Doppler terminal fall speed VD T. An estimate of the VD Tcan be provided by the sedimentation velocity best estimate variable in the L2A C-CD data product (Kollias et al. 2023). Interestingly, two regions with 10 dBZ echo top height well above the 10 km altitude exhibit such dynamical features. At 950-975 km along-track, a deep and coherent dynamical structure is observed, characterized by strong upward motions extending from 8 to 14 km. This vertically oriented feature represents a deep convective updraft and is collocated with the highest 10 dBZ echo top heights. The WAIR within this140 convective updraft is strong enough to cause velocity folding, depicted as a red patch of Doppler velocities embedded within the negative Doppler velocity cluster. 6 https://doi.org/10.5194/egusphere-2025-1914 Preprint. Discussion started: 15 May 2025 c Author(s) 2025. CC BY 4.0 License. Figure 3. (a) CPR reflectivity during a deep convective event system on September 18, 2024 over Western Africa (Frame 1752E). The blue circles indicate the height where multiple scattering effects become important. The vertical dashed lines indicate the locations where CPR profiles will be shown in later figures. (b) the CPR Doppler velocity measurements after a 4-km along-track integration (Kollias et al. 2023). Positive Doppler velocities indicate hydrometors’ movement towards the ground. The black contour indicates the area where the 4-km CPR Doppler velocity standard deviation exceeds 2 ms −1. A box of 3 km along-track by 2 km in range is used for the estimation of the standard deviation. The complexity of the VDprofiles in deep convection is examined using a sample deep convective cloud (DCC) observed by the CPR (Fig. 3). The DCC is located between 300 and 350 km along track and is characterized by overshooting cloud tops reaching up to 17 km in altitude. Strong attenuation is observed (Fig. 3a), and the smooth appearance of radar reflectivity145 echoes extending to and below the surface indicates the presence of moderate multiple scattering effects (Battaglia et al. 2010). Regions contaminated by multiple scattering are currently identified in the C-FMR product (Kollias et al. 2023) using a simple flagging approach based on the methodology proposed by Battaglia et al. 2011a. The blue-filled circles denote the height at which multiple scattering effects on radar reflectivity are expected to become significant. To correctly interpret Doppler velocities in deep convection, it is essential to investigate the influence of multiple scattering on the Doppler signal (Battaglia150 et al. 2011b). However, since this is not the focus of the current study, our interpretation will be limited to the portion of the VDprofiles above the height where multiple scattering effects begin to become significant. The DCC VDmeasurements are shown in Fig. 3b. The VDprofiles substantial variability, with regions of both positive and negative values. The range of VDvalues span the full Nyquist interval [-VN: +VN]. The convective VDprofiles are characterized by frequent Doppler velocity aliasing. Fig. 3b presents the 4-km averaged VD. Velocity aliasing is even more pronounced at155 the 1-km averaged VD. The observed VDvariability serves as a strong indicator of the presence of convective updrafts and 7 https://doi.org/10.5194/egusphere-2025-1914 Preprint. Discussion started: 15 May 2025 c Author(s) 2025. CC BY 4.0 License. Figure 4. (a) the CPR reflectivity profile at along-track distance of 309 km. The blue filled circles section of the CPR reflectivity profile indicate the CPR range gates where the Doppler velocity estimates are considered unaffected by multiple scattering. The green triangle indicates the height of the maximum radar reflectivity. (b) The 4-km CPR Doppler velocity profile (green filled circles) and the 1-km CPR Doppler velocity profile (gray filled circles). The black dashed vertical lines indicate the CPR Nyquist Doppler velocity. (c) The unfolded 4-km CPR Doppler velocity profile (green filled circles) and the unfolded 1-km CPR Doppler velocity profile (gray filled circles). The black dashed vertical lines indicate the CPR Nyquist Doppler velocity downdrafts. In Figure 3b, the black outline highlights regions where the standard deviation of Doppler velocity exceeds 2 m/s. The standard deviation is calculated within a moving window of 3 km horizontally and 2 km vertically, centered on each pixel, to capture Doppler velocity variations in both the along-track and across-track Doppler velocity directions. Two example profiles corresponding to the along-track locations indicated by the black dashed lines in Fig. 3b are analyzed160 here to explore the complexity of the VDin deep convective cores. The first profile is shown in Fig. 4. The CPR reflectivity profile is presented in Fig. 4a. The blue-filled circles mark the CPR range gates where Doppler velocity estimates are considered unaffected by multiple scattering. Additionally, VDestimates near the cloud top are excluded if CPR reflectivity falls below –15 dBZ. The maximum reflectivity is observed at an altitude of 11 km, more than 5 km below the cloud top. The corresponding VDprofiles, averaged over 1-km and 4-km along-track intervals, are shown in Fig. 4b. The black dashed lines denote the165 CPR Nyquist velocity bounds, while the vertical yellow line indicates zero Doppler velocity. As expected, the 4-km-averaged VDexhibits lower variability with height compared to the 1-km VDestimates. This vertical correlation is expected, given that the CPR pulse length is 500 m and VDis estimated every 100 m. 8 https://doi.org/10.5194/egusphere-2025-1914 Preprint. Discussion started: 15 May 2025 c Author(s) 2025. CC BY 4.0 License. Figure 5. (a) The CPR reflectivity profile at along track distance of 321 km. The blue filled circles section of the CPR reflectivity profile indicate the CPR range gates where the Doppler velocity estimates are considered unaffected by multiple scattering. The green triangle indicates the height of the maximum radar reflectivity. (b) The 4-km CPR Doppler velocity profile (green filled circles) and the 1-km CPR Doppler velocity profile (gray filled circles). The black dashed vertical lines indicate the CPR Nyquist Doppler velocity. (c) The unfolded 4-km CPR Doppler velocity profile (green filled circles) and the unfolded 1-km CPR Doppler velocity profile (gray filled circles). The black dashed vertical lines indicate the CPR Nyquist Doppler velocity. Here, we focus on interpreting the VDestimates within the section identified as having reliable Doppler velocity retrievals. Beginning with the 4-km profile: near the cloud top, the VDis positive, indicating the presence of an updraft. Below 14 km,170 the VDturns negative, which may indicate the presence of large hydrometeors falling, a downdraft, or a combination of the two, resulting in an apparent downward motion. The abrupt jump of more than 10 m/s in the profile at 12.5 km is attributed to velocity aliasing. In general if the absolute value of the difference between two consecutive Doppler measurements exceed the Nyquist velocity, then adding ±2VNto one of the velocity produces a smoother profile. Due to the noisiness of the measurement the identification of a fold is not so straightforward and there will be some ambiguity for points with jumps in vD 175 close to vN(e.g. for the 4-km integration values between VN−1and VN+1 m/s). In this example the difference is much larger, so folding is identified unambiguously and unfolding is straightforward. All the segment of the profile between 9 and 12.5 km is therefore aliased; Fig. 4c shows the unfolded 1-km and 4-km VDprofiles. The aliased negative sections of the 4-km profile have been corrected by adding 2VN. The unfolded 4-km profile displays a smooth vertical structure. Except for a small region 9 https://doi.org/10.5194/egusphere-2025-1914 Preprint. Discussion started: 15 May 2025 c Author(s) 2025. CC BY 4.0 License. Figure 10. Successive images depicting the time evolution 10 minutes before (a), 5 minutes before (b), closest in time (c), 5 minutes after (d) and 10 after (e) EarthCARE overpass 2530D on November 7th, 2024, zoom on cell 2. The colors represent the brightness temperature from channel 9 (10.8µm), measured by MSG rapid scans. Black solid line represent the ground track of EarthCARE, corrected for parallax (dashed line is the original ground track). Red markers correspond in shape to Fig. 8a. The black star is the position of the minimum brightness temperature that is tracked. EarthCARE mission, equipped with a Doppler-capable radar, fills this critical observational gap and marks the beginning of a new era of satellite-based radar measurements to improve our understanding of convective dynamics. Before launch, there were numerous questions regarding the quality of Doppler velocity measurements in deep convection, particularly due to anticipated challenges such as strong attenuation, multiple scattering, non-uniform beam filling (NUBF) effects, the limitations imposed by a narrow Nyquist velocity range, and the complexity introduced by the vertical and hori-305 zontal variability of convective cores. In this study, CPR transects across various convective systems are analyzed to assess and illustrate the impact of these challenges on the interpretation of Doppler velocity profiles. The availability of Doppler velocity measurements from space provides valuable new insights into the presence, as well as the horizontal and vertical extent, of convective updrafts and downdrafts. Doppler velocity-based detection of convective cores is compared with traditional reflectivity-based methods. This comparison is expected to inform a revision of the detection310 criteria used in previous spaceborne radar studies. Furthermore, when combined with co-located infrared observations from geostationary satellites, CPR Doppler measurements offer new perspectives on the use of cloud-top cooling rates—computed as time derivatives of brightness temperature—as proxies for convective intensity. Some preliminary conclusions of this work are summarized in the following.315 16 https://doi.org/10.5194/egusphere-2025-1914 Preprint. Discussion started: 15 May 2025 c Author(s) 2025. CC BY 4.0 License. Figure 11. Minimum brightness temperature (in K) within the cell, as detected and tracked with tobac. Red dashed line corresponds to the EC overpass time. (a) Cell 1. (b) Cell 2. 1. The first images of Doppler velocities measured by the EarthCARE Cloud Profiling Radar (EC-CPR) offer an unprecedented view of convective motions on a global scale. While these images immediately reveal the presence of convection, the quantitative interpretation of the CPR signal—such as the estimation of updraft and downdraft velocities or convective mass fluxes—will require further analysis. This need arises from the inherent complexity of convective dynamics, compounded by signal noise and the limitations imposed by the narrow Nyquist velocity range.320 The CPR Doppler velocity measurements will serve as the foundation for a dynamics-based convection identification algorithm, designed to augment existing reflectivity-based detection methods. As demonstrated in the case study, parameters such as the standard deviation of Doppler velocity computed within a 3 km horizontal by 2 km vertical window, or the frequency of Nyquist velocity foldings, can serve as reliable indicators of convective activity. 2. The development of a robust algorithm for unfolding CPR Doppler velocity (VD) measurements in deep convective325 clouds is currently underway. The first step is to characterize the complexity of the VDfield and to identify the primary sources of velocity discontinuities in deep convection. Initially, the focus will be limited to convective profiles exhibiting fewer than three Doppler velocity foldings at the 4-km along-track resolution—an approach expected to encompass more than 99% of the observed CPR VDprofiles. In cases where velocity aliasing is not observed in the 4-km averaged VD, but is present in the 1-km averaged profile, the 4-km averaged VDcan be used as a weak constraint to unfold the330 1-km averaged VDby minimizing the difference between the two. In more complex cases, such as those shown in this study, the morphology of the CPR reflectivity profile will be used to determine the vertical continuity of the convective 17 https://doi.org/10.5194/egusphere-2025-1914 Preprint. Discussion started: 15 May 2025 c Author(s) 2025. CC BY 4.0 License. column. In addition, VDestimates at 500 m (native CPR along track resolution), 1-km or 4-km will be combined for the estimation of the unfolded CPR VDprofile. 3. The CPR provides a unique capability for observing embedded convection and sub-kilometer-scale convective cells,335 thereby overcoming key limitations of convective observations derived from geostationary imagery. In particular, convective motion estimates based on cloud-top cooling rates are effective primarily for updrafts that are both comparable in size to the geostationary sensor’s resolution (typically larger than 2 km at mid-latitudes) and located near the cloud top. As such, this method is generally limited to convective cells in the early stages of development or to those exhibiting overshooting tops..340 4. Geostationary imagery, on the other hand, offers significant potential for providing the spatio-temporal context of convection—such as whether it is part of a mesoscale system or an isolated cell, and whether it is in the early, mature, or decaying stage of its lifecycle. Additionally, geostationary observations are well-suited for quantifying updraft strength in isolated convective cells, where the time series of minimum cloud-top brightness temperature is expected to be strongly correlated with the intensity of the updraft.345 The Doppler capability of EarthCARE’s Cloud Profiling Radar (CPR) represents a major innovation, enabling the direct observation of vertical air motions and the terminal fall speeds of hydrometeors. Nonetheless, substantial effort is still required to fully harness this capability and convert these measurements into actionable insights for atmospheric science and modeling. As a next step, a new convection classification framework will be developed using Doppler velocity and radar-derived features. Once established, this classification—when integrated with synergistic geostationary observations—will support the350 systematic identification of convective regimes and their associated characteristics. This framework will then be applied to generate global-scale statistics. These efforts will significantly enhance our understanding of convective dynamics at the global scale and are expected to inform and validate high-resolution weather and climate models. Acknowledgements. The research by AG has been supported by the PANGEA4CalVal project (Grant Agreement 101079201) funded by the355 European Union. AB has been funded by the Space It Up project funded by the Italian Space Agency, ASI, and the Ministry of University and Research, MUR, under contract n. 2024-5-E.0 - CUP n. I53D24000060005. PK and BPT were supported by the European Space Agency (ESA) under the Clouds, Aerosol, Radiation – Development of INtegrated ALgorithms (CARDINAL) project (RFQ/3-17010/20/NL/AD) and the National Aeronautics and Space Administration (NASA) under the Atmospheric Observing System (AOS) project (Contract number: 80NSSC23M0113).360 Author contributions. 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