Launched on the 28 May 2024, the Earth Cloud, Aerosol, and Radiation Explorer (EarthCARE) satellite measures aerosol, cloud, and precipitation profiles alongside their co-located radiative flux at the global scale . To this end, it is equipped with a Doppler cloud radar, an atmospheric lidar, a multispectral imager, and a broadband radiometer. The observations from these instruments will be used to evaluate the representation of aerosols, clouds and precipitation in weather forecast and climate models, and help improve their parametrization schemes for the next 10 years.

The onboard Cloud Profiling Radar (CPR) operates at 94 GHz frequency (W-band) and is the first space-borne cloud radar measuring Doppler velocities. EarthCARE CPR is capable of measuring reflectivities down to -35 dBZ in the troposphere, improving 5 dB in sensitivity with respect to the previous CloudSat CPR . At the early stage of a satellite mission it is essential for its data and products to be verified and validated. The work presented in this article is part of this CAL/VAL (calibration/validation) effort. Coupled with other CAL/VAL methods (ocean backscattering, airborne radar comparisons, ground based calibrators) , this work aims to ensure that the reflectivity values measured by the CPR are well calibrated.

The Aerosol, Clouds and Trace gases Research Infrastructure (ACTRIS) is a pan-European research infrastructure producing high quality data and services on short-lived atmospheric constituents and the processes leading to their variability in natural and controlled atmospheres . ACTRIS is structured around three core themes – aerosols, trace gases, and clouds – and relies on Central Facilities, specializing in each of these scientific areas. Among them, the Centre for Cloud Remote Sensing (CCRES) coordinates expertise in cloud remote sensing.

ACTRIS cloud remote sensing sites are mainly distributed in Europe, as shown in the Fig. . Most of the sites are equipped with Doppler cloud radars, microwave radiometers, disdrometers, Doppler lidars and ceilometers. This instrumental diversity allows for a variety of products, using the instruments synergy .

Even with the spatial extension of the ACTRIS network it is, in general, unlikely to have exactly co-localized observations between CPR and ground-based cloud radars. For each site, we expect at least a few tens of kilometres of distance between orbital overpasses and each ACTRIS National Facility (NF). Another consideration is that the majority of ACTRIS ground-based radars are zenith pointing only (∼ 75 %). Even for sites that do have scanning capabilities, co-localization would remain unlikely due to the limited range of cloud radars (≲ 20 km), caused by the strong atmospheric attenuation at their operation frequencies (mainly in the 35 and 94 GHz bands).

We thus adopted a statistical approach to compare the EarthCARE CPR and ground reflectivities. Earlier studies have already proposed methods to do such comparisons, using CloudSat CPR as the space instrument. Both and compare satellite reflectivity profiles, sampled in a given radius around the ground-site of interest, with ground profiles in a fixed time-window around the overpass time. Bias is estimated as the difference between the mean profiles from both sources. These approaches are already capable of providing bias estimates (and hence the calibration difference) between the space and ground instruments, but we find that they can still be improved to provide more robust results in our use-case.

A key limitation of the current approaches is the absence of uncertainty estimates when evaluating bias values. To assess EarthCARE calibration, we compare CPR reflectivity with ACTRIS ground-based radars, which provide calibrated measurements with associated uncertainty estimates. Without accounting for the uncertainty introduced by the satellite-ground comparison algorithm, it is impossible to accurately evaluate the overall uncertainty of our EarthCARE calibration estimates. The quantification and propagation of the uncertainty is then a core requirement for the developed algorithm.

Another key improvement lies in the validation process. For EarthCARE, it is crucial to obtain a reliable calibration estimate that is independent of the characteristics of any single site or specific ground radar. Taking advantage of the calibration homogeneity established by ACTRIS-CCRES, we propose to validate EarthCARE calibration bias estimates using multiple ground-based sites. This enables to further scrutinize the results through closure: having consistent bias estimates from all sites would significantly strengthen confidence in both the data processing methodology and the reliability of the EarthCARE bias estimate.

Another necessary improvement for our use case is the quantification of the minimum sampling time required to ensure reliable comparisons. Currently, no recommended minimum period exists, and given the recent launch of EarthCARE, insufficient data sampling could lead to skewed statistical results. There is a need for a study on how impactful the comparison duration can be on the results.

Finally, our algorithm has to be compatible with EarthCARE and ACTRIS data formats and processing pipelines. This requires adapting the time co-localization in data selection to account for the lower count of vertical profiles compared to raw data from the ground radars. Compatibility with CloudSat data is also desirable to enable historical comparisons with ACTRIS data.

This article is structured as follows. Section  is dedicated to the description of calibration strategy of the ACTRIS ground radar network. The satellite and ground datasets are described in Sect. . The implemented algorithm and considerations made in its development are presented in details in Sect. . The reflectivities of EarthCARE are validated using the calibrated radars from the ACTRIS network, as described in Sect. . Section  presents the evaluation of the impact of the time period considered for the evaluation of the satellite-ground bias.

One of the main ACTRIS objectives is to assure reliable, comparable data at all sites of its instrument network. The Center for Cloud Remote Sensing (CCRES) is the ACTRIS expertise center supporting the operations of cloud remote sensing instrumentation within the network . A goal of CCRES is to define a set of methodologies to guarantee the highest quality of measurements and data products. CCRES also oversees the deployment and further development of experimental systems dedicated to cloud observation.

CCRES is at present working on a vast calibration effort for the entire ACTRIS cloud radar network. To this end, the strategy involves two parallel activities: (i) To perform the calibration of reflectivity measurements for each radar in the network, and (ii) The implementation of a calibration tracking system which enables detection and quantification of calibration drifts over time.

The reflectivity calibration is done as follows:

A portable reference radar (BASTA-CCRES, W-band, ) is calibrated at a CCRES facility using a reference target . The use of targets characterized in an anechoic chamber enables to reach a calibration uncertainty of 0.8 dB on reflectivity.

As the described calibration campaign are time consuming, it is necessary to track any change in the calibration of the ground radars. A calibration tracking setup is thus implemented at each ACTRIS NF, in parallel to the calibration campaigns. The setup requires the installation of a co-located disdrometer, with the objective of independently measuring the reflectivity of stratiform precipitating particles. These reflectivity values are then compared with radar observations to detect sudden calibration biases and enable corrective actions, ensuring a reliable data set (e.g. ).

Currently CCRES has completed cloud radar calibration campaigns for the stations of Palaiseau, Jülich, Leipzig and Lindenberg. All radars were calibrated using the BASTA-CCRES reference radar, allowing us to perform closure tests on our space-ground comparison algorithm. Table  shows each site coordinates, radar types, number of overpasses within 200 km for each satellite cycle of 25 d, the time period analyzed and the introduced correction coefficient with the resulting ground-radar calibration uncertainty. The two RPG radars of the Jülich site were installed after the calibration campaign took place. Hence their correction coefficients are calculated based on the MIRA radar, which was the only radar in operation at the time of the calibration campaign.

In this work, we compare the reflectivity profiles of the EarthCARE L2a CPR FMR products with those sampled by ground-based radars of the ACTRIS network. The following sections present the datasets, and introduces the products that are relevant to our study.

As mentioned in Sect.  the CCRES component of ACTRIS is performing inter-comparison campaigns using a reference radar moved from site to site. From these campaigns, a correction coefficient is evaluated for the radars of each sites. The radars of the sites of Palaiseau, Jülich, Leipzig and Lindenberg (see Table ) went through this calibration process. Moreover the ground radars did not go through any changes on the time periods considered.

To validate the algorithm and the CPR reflectivities, we compare the satellite-ground bias estimated for each of these sites, taking account of the calibration correction coefficients. Figure  shows a summary of the EarthCARE-ground biases estimated for the radars of the calibrated sites. These biases were evaluated on a time period from 20 July 2024 to 18 November 2025 (or on a smaller time period within), collecting satellite data in a 200 km radius from the site.

From Fig. , we see most individual biases are close to 0 dB. The largest evaluated bias is for the RPG of the Lindenberg site with an evaluated bias of 1.5±1.7. Looking more closely at these results (see Appendix ), the algorithm selects different height zones for the bias estimation. Each of the zone is correct for the bias estimation but they are not coincident, leading to a bias estimate with a higher uncertainty. As the calibration of this radar has been stable and no changes occurred on it during the whole time period, we think that this high bias could come from differences in the clouds observed by both radars. Having more satellite data should help to reduce this uncertainty.

The bias of the radar of Palaiseau is the second largest and only bias for which the uncertainty does not cross the 0 bias line. For this bias, a time period of 6 months was used for the comparison, when at least 9 months was used for the other radars. This observation leads toward the hypothesis that the bias is sensitive to the time period used for its estimation. This hypothesis is explored in more depth in Sect. .

Finally, when calculating the mean weighted to uncertainty

The mean weighted to uncertainty is calculated as follows. Firstly, the weight related to the bias uncertainty is evaluated with: wi=1σi (with σi the uncertainty). The weighted mean is then given by: μ=∑wibi∑wi (with bi the bias). The uncertainty associated to this weighted mean is: σμ=1∑wi.

An important limiting factor for the comparisons is the availability of satellite data. This limitation stems from the fact that some factors like the number of overpasses or the presence of ice clouds during the overpasses cannot be controlled. At present there are no recommendations on the minimum comparisons time (or number of overpasses) needed to obtain reliable bias estimates. To improve the understanding of the impact of the sampling time on the ground-satellite reflectivity comparison, we propose to track the evolution of the bias over a period of time through a time series. We firstly evaluate a bias on the longest time period to act as a baseline. Biases on smaller time periods within the baseline are then evaluated to compare the effects of different time sampling durations on the bias. The following time series focus on the variations of the smaller period biases with respect to the baseline, thus not showing the absolute bias values.

To have a long enough time period to study these effects of time sampling we compared CloudSat reflectivities to the reflectivities from the MIRA radar of the Lindenberg site. Both of the radars were active during a period of less than 4 years spanning from August 2012 to January 2016. As mentioned in Sect. , EarthCARE and CloudSat CPRs are overall similar one to the other, with CloudSat CPR being less sensitive. We thus applied our methodology to CloudSat data, using 2B-GEOPROF R05 reflectivity data and the DARDAR product as a classification to remove profiles with liquid water . Figure  shows the CloudSat reflectivities compared with the ground reflectivities of the MIRA radar from the Lindenberg site.

Looking at Fig. , we observe a time changing bias between the two instruments. Previous studies have showed the accuracy of the calibration of CloudSat's CPR . The quality and stability of this calibration was used in later to evaluate the calibration of ground radars . As the time period used here (2012–2016) is in the same time span used by , we assume that the calibration of CloudSat was stable and that the majority of the CloudSat-ground bias variations come from the ground radar. This assumption is reinforced by the fact that the ground radar has undergone hardware changes during the time period considered. The variability in the bias can be attributed to a misalignment of the phase correction position (PCP) in signal processing chain of the MIRA magnetron. If PCP position is not optimal or drift over time (due to ageing), the misalignment can lead to a suboptimal phase-to-phase correction and consequently in systematic underestimation of the radar reflectivity. The 2 dB bias increase after September of 2013 could be attributed to this effect . Another variation in bias is noticeable with the bias for ±4.5 months and ±6 months slowly increasing around January 2015. This indicates that the radar started to reduce it's transmitted power over time. The change in power was noticed by the on site team at the time and decided to change the magnetron and a faulty waveguide in 2016. This indicate that we are able to monitor changes in the calibration from the ground.

Figure  shows the comparison of EarthCare data with the MIRA radar from the Jülich site on a period of a little less than a year and a half spanning from 20 July 2024 to 18 November 2025.

Looking at Fig. , the biases from the ±4.5 months and ±6 months are mainly within the uncertainty of the baseline. Considering the ±3 months, half of the biases estimated are out of the baseline uncertainty and the algorithm is not able to infer a bias point for October 2024 (each ±3 months points is 2 months apart, hence why the first point on the figure is mid December 2024). The ±3 months time period thus seems less suitable to estimate a solid bias value. However we observe a decrease in the bias estimate for the 3 time periods considered. This phenomena is also observed for the other radars of the Jülich site and could be the consequence of changes in the target observed at this site, as EarthCARE and the ground radars calibration are regularly monitored. Moreover a change in the calibration of the three ground radars at the same time seems unlikely. This variation could indicate a seasonal variability of the estimated bias, however we do not have enough data at the moment to conclude on this variation.

From Figs.  and , we assess that the minimum time period needed to obtain a solid reflectivity comparison would be ±4.5 months (ie: 9 months time period). This ±4.5 months time period give biases in the uncertainty of the baseline most of the time. The ±3 months (ie: 6 months) time period is much more variable and in some cases does not give enough data to be able to evaluate a bias through our method. In the frame of the monitoring of a ground network, the ±3 months bias could then be used as a preliminary value of the bias to be reinforced with longer time periods. The ±6 months (ie 1 year) time period, seems to be the most precise, showing the less variations to the baseline and always being within the baseline uncertainty. This ±6 months time period, thus seems to be optimal to evaluate a solid bias value. Moreover, both CloudSat and EarthCARE results confirm the capacity of our method to see changes in the calibration of ground radar, and thus the possibility to use it to monitor the calibration of zenith looking ground radars as it is done with CloudSat in .

This article presents a new algorithm that enable improved comparisons of calibration between satellite and ground-based cloud radars. The algorithm has been developed to check the reflectivity retrievals of the EarthCARE mission, by using calibrated sites of the European ACTRIS infrastructure. Its operating principle relies on the statistical comparison of vertical ice cloud reflectivity profiles.

To compare, we firstly identify a time period where no hardware or calibration changes have been registered on the ground-based radar. We have set on six months as the shortest period usable for this study. Then, all the ground-based vertical ice-cloud profiles sampled in the period are kept for the next statistical analyses. Similarly, all satellite ice-cloud profiles sampled within 200 km of the ground-site in the same time period are kept. Ice cloud profiles are distinguished from liquid using the target classifications from CloudNET and EarthCARE L2a CPR TC products, for the ground and space-based radars respectively. The satellite and ground reflectivity products also provide corrections for atmospheric gas attenuation.

Ground-based data are resampled to match EarthCARE vertical resolution, enabling to fit both lines of sight at the same height bins with a Gaussian function. The parameters from these fits are then used to select the height bins of most statisticall comparability. The reflectivity bias between EarthCARE and the ground radars is then evaluated at the selected heights. The method takes account of the uncertainty contributions from the statistical comparison, and from the ground-based radar calibration, to provide a calibration bias estimate with an associated uncertainty value.

The algorithm is tested by closure with seven calibrated radars from the ACTRIS network. All seven sites show results compatible with the assertion that they are equally calibrated, providing coherent bias estimates within uncertainty.

On a time period of a year and a half, we infer a mean bias -0.2±0.4 dB between EarthCARE L2a CPR FMR product and the average calibration of the ground-based radars. This estimate with very low uncertainty is achieved with a weighted average of the seven calculated biases. Indeed each individual estimate has an uncertainty of ≈1 to 1.5 dB, highlighting the usefulness of having an homogeneous ground-based network for satellite calibration and validation studies. Due to the lower bias value found with respect to its uncertainty, we can conclude that the L2a EarthCARE CPR FMR product of the BA baseline is well calibrated, providing unbiased reflectivity retrievals.

During the development of this study, it was found that a critical parameter for the reliability of the comparisons is the availability of data for both ground and space radars. This motivated the use of all vertical profiles for ground-sites, and to investigate how the length of the time period affects the bias results. A remarkable conclusion from the first measure is that space and ground data remain comparable even if they are not co-localized in time. This suggests that EarthCARE is indeed capable of sampling typical reflectivity values for a given land surface, opening the door to future research into the climatological applications of its measurements. Ground-based networks such as ACTRIS could be used to validate the methods and results used in such climatological studies.

Regarding the impact of the time period length, from comparisons of ACTRIS with CloudSat and EarthCARE, we conclude that a minimal comparison time period of 9 months is recommended for European latitudes (mid-latitude sites). The time series of EarthCARE bias estimates also indicate a possible seasonal variability of the bias estimate, yet there is not enough data to confirm or reject this hypothesis. Investigations on the relevance and source of this possible variability will be possible when a few years of EarthCARE data is collected.

As future work, it could be interesting to research new ways of reducing the time required to make reliable estimations of the calibration bias. This would benefit the resolution of the bias time series, enabling a quicker detection of calibration changes.

One possible approach could be the use of an optimized radius for each ground-based site. We think that a more fine determination of this parameter could enhance the speed at which comparable data is added to the global statistics used in the comparisons. Radius optimization could be done on a statistical basis, or even for single individual overpasses, depending on cloud homogeneity and dominant winds at each site.

Another point for future research could be the study of the impact of latitude in the comparisons. The latitude of the ground-based site is highly relevant for two reasons: sites on near pole latitudes have more satellite overpasses, in the order of one per day instead of one per week, significantly increasing the rate of data collection. Data collection increase if further amplified by the nature of high latitude sites, which are also more likely to have ice clouds, and less likely to have liquid precipitation when compared with near equator sites. For the same 200 km selection radius for satellite data, we would expect near pole radars to need half or even less sampling durations to obtain as reliable comparisons as what we obtained for the European sites.

Moreover, the use of the EarthCARE synergistic classification (AC-TC) should be investigated when the product will have undergone further validation. It could be used to filter profiles containing supercooled liquid water. This filtering should however be investigated carefully in order to be precise as both ground and satellite lidars have a different geometry of observation.

Overall, we think that for future optimizations of satellite versus ground radar comparisons, it will be necessary to consider the interplay between sampling time, radius for satellite data selection, overpass frequency and the cloud characteristics at the latitude of each site.

To summarize, the validated methodology presented in this article will allow the community to continuously check EarthCARE reflectivities by using the ACTRIS cloud remote sensing network or other similar structures. We also think that the algorithm can be used in both ways, enabling the estimation of ground radar calibration biases by using EarthCARE as the reference. Such application would prove beneficial for remote ground-sites, such as Arctic observatories, or the new AWACA sites deployed in Antarctica . Finally, the method can be applied on cloud radar networks in general to monitor their calibration homogeneity and stability, continuing and improving on the developments that started with CloudSat in the past.