Vertically Resolved Convective–Stratiform Echo-Type Identification and Convectivity Retrieval for Vertically Pointing Radars
1. Introduction
The terms convective and stratiform are used to describe different types of clouds and precipitation. Convective processes that lead to convective clouds and precipitation are characterized by vertical air motions that equal or exceed the fall speed of frozen hydrometeors when averaged over a certain horizontal area (Houze 2014). In radar echoes they are depicted as vertically oriented columns of high reflectivity. In regions with stratiform clouds and precipitation the vertical air motion is generally small compared to the fall speed of frozen hydrometeors. The particles are suspended midair and appear as horizontally uniform layers in radar images. At the melting layer, which is generally located up to a few hundred meters below the 0°C freezing level (Romatschke 2021), the so-called radar bright band is often visible as a narrow horizontal layer of intense echo. A discontinuity in the radar radial velocity field at the melting layer indicates an increase in vertical velocity (Houze 2014) as the particles change from the frozen to the liquid phase.
Since convective and stratiform clouds and precipitation indicate fundamentally different physical and microphysical processes, and since radar is a primary tool for distinguishing these two categories of clouds and precipitation, there is a long history of automating the detection and separation of convective and stratiform radar echo types. Many of these algorithms rely on identifying the characteristics of the horizontal versus vertical structure of the echoes. Most algorithms were developed for three-dimensional (3D) radar grids (e.g., Steiner et al. 1995; Biggerstaff and Listemaa 2000; Awaka et al. 1997; Powell et al. 2016) but some exist for vertically pointing radars (e.g., Thurai et al. 2016; Haynes 2018), which mostly rely on the detection of the bright band. A different approach is the use of machine learning techniques (Anagnostou 2004; Yang et al. 2013; Foth et al. 2021; Wang et al. 2021). Although, on a fundamental level, convective and stratiform processes are defined based on vertical air motions, most partitioning algorithms rely on clues from reflectivity, since observations of vertical velocity are rarely available in 3D radar grids. Doppler velocity from vertically pointing radars on the other hand is used as a proxy for vertical air motion in echo-type classification algorithms (Geerts and Dawei 2004; Foth et al. 2021).
An alternative approach to echo-type classification uses microphysical parameters such as the median droplet volume diameter and drop size distributions. This method was originally developed for disdrometer measurements (Caracciolo et al. 2006; Bringi et al. 2009; Thurai et al. 2016) but has recently also been applied to radar data (Garcia-Benadi et al. 2020). The classifications provided by this technique are limited to regions with liquid hydrometeors. The implementation of this technique is also complicated as the underlying microphysical properties are not measured by the radar and must be retrieved. Hence, the necessary retrieval algorithms add uncertainties.
Previously, convective and stratiform echo classifications were only provided on the horizontal dimensions so that all points in a vertical radar-echo column would be classified identically. One exception is the microphysical method by Garcia-Benadi et al. (2020) which provides classifications at all levels, but only for regions with liquid particles. The recently introduced Echo Classification from COnvectivity (ECCO) algorithm by Dixon and Romatschke (2022) provides echo type that is resolved in both the vertical and horizontal dimensions for radar echo from all hydrometeor types. The ECCO algorithm was developed for 3D radar grids and two-dimensional (2D) grids on the horizontal axes. We use data from an airborne cloud radar to adapt the original ECCO algorithm (which we will call ECCO-3D hereafter) for vertically pointing radars (which we will call ECCO-V hereafter), that is, for 2D radar grids with a vertical and a horizontal (or time) axis. ECCO-V is then demonstrated using data from spaceborne and ground-based radars. As vertically pointing radars can only sample vertical cross sections through storms, adding vertically resolved echo-type classifications is of particular interest, because otherwise the convective–stratiform classification is limited to only one dimension on the horizontal (time) axis (Haynes 2018). An additional advantage of the ECCO method is that it not only provides a vertically resolved classification but it also introduces the concept of convectivity, a quantitative measure of how convective a certain grid location is, as opposed to the purely qualitative binary classification of either convective or stratiform for a vertical column provided by previous methods. The ECCO-V algorithm is publicly available at https://github.com/NCAR/cloudSat_ecco-v.
2. Data
The High-Performance Instrumented Airborne Platform for Environmental Research (HIAPER) is a Gulfstream V aircraft deployed in field campaigns by the National Center for Atmospheric Research (NCAR) for the National Science Foundation (NSF). One of the instruments that can be requested by the scientific community on the aircraft is the HIAPER Cloud Radar (HCR), a 94-GHz (W-band) cloud radar that is mounted in an underwing pod (Vivekanandan et al. 2015; Romatschke et al. 2021). It has a peak power of 1.6 kW and operates with a pulse repetition frequency (PRF) of 10 kHz and a beamwidth of 0.73°. The radar’s sensitivity is −37.0 dBZ at a signal-to-noise ratio (SNR) of −10 dB and at 1-km range. HCR samples at a range resolution of ∼20 m and a time resolution of 10 Hz, which at typical aircraft speed translates to a ∼20-m horizontal resolution, i.e., effectively a 20 m × 20 m grid spacing. HCR has polarimetric and Doppler capabilities and, among other variables, provides the first two standard radar moments reflectivity (DBZ) and radial velocity (VEL), both of which are used in ECCO-V. Melting-layer altitude detections for each HCR time step (Romatschke 2021) and ERA5 temperature data (European Centre for Medium-Range Weather Forecasts 2019) interpolated onto the HCR time–range grid (Romatschke et al. 2021) are also included in the standard HCR datasets.
HCR has been deployed in several major field campaigns ranging in location from the tropics to the Southern Ocean (Albrecht et al. 2019; McFarquhar et al. 2021; Fuchs‐Stone et al. 2020), sampling a variety of cloud types from large stratocumulus fields to deep tropical convection. ECCO-V was developed with HCR datasets and tested on data from all major HCR field campaigns to ensure its adaptability to different cloud types and meteorological regimes.
To broaden the applicability of the algorithm, we adapted it for other types of radars. The spaceborne Cloud Profiling Radar (CPR; Im et al. 2005) on board the CloudSat satellite is an ideal candidate for the deployment of this convective–stratiform echo-type algorithm. Like HCR, it is a 94-GHz nadir-pointing W-band radar. It samples clouds around the globe with a vertical resolution of 500 m and an along-track resolution of 1.7 km. We use radar reflectivity from the 2B-GEOPROF product (Marchand et al. 2008), and the convective–stratiform flag and freezing level from the 2C-PRECIP-COLUMN product (Haynes 2018) from 1 July to 31 August 2015.
A version of the algorithm suitable for ground-based vertically pointing cloud radars was tested on the millimeter wavelength cloud radar (MMCR) system located at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) facility in Oklahoma. MMCR is a zenith pointing 35-GHz Ka-band radar, and we use reflectivity data from scan mode 3, which was collected on 17 August 2008, at an ∼87-m vertical resolution and a ∼5-s time resolution.
See the data availability statement for information on how to access the different datasets.
3. Algorithm description
a. Convectivity
As outlined in the introduction, convective radar echoes show vertically oriented features while stratiform echoes present as horizontally uniform layers. Hence, a good approach to identifying convective regions is to test how homogeneous or heterogeneous the radar echoes are on the horizontal axis. Following the ECCO-3D method, we calculate a texture field on the horizontal time dimension as outlined in the following.
We start with DBZ and complete one preprocessing step where we fill in all clear-air regions with reflectivity values from their closest nonmissing neighbor in the time dimension, to avoid artifacts at the outer edges of the cloud echoes.
Reflectivity texture is calculated over a certain horizontal extent and the following operations are performed on specified time segments in the time dimension. For the HCR grid resolution, a running window over 10-s time segments yields good results. In Fig. 1, example time segments in a stratiform and a convective cloud (black lines A and B in Fig. 1a, respectively) are shown. The black dots (Fig. 1a) indicate the midpoint for which reflectivity texture is calculated from the respective time segment. These two example time segments will be used to demonstrate the steps of the calculation.
(a) Reflectivity from HCR data sampled from 1239 to 1244 UTC 27 Sep 2019, during the OTREC field campaign off the west coast of Costa Rica. (b),(c) DBZ (blue line) and DBZfit (red line), and (d),(e) DBZcorr (left y axis) and DBZadj (right y axis) for the time segments shown with black lines (left) A and (right) B in (a). Values of TDBZ and scaled TDBZ for the pixels marked with a black dot on time segments A and B in (a) are given in (d) and (e), respectively.
Citation: Journal of Atmospheric and Oceanic Technology 39, 11; 10.1175/JTECH-D-22-0019.1
The ECCO-3D algorithm successfully uses TDBZ alone but since the fundamental definition of convective versus stratiform processes is based on vertical air motion, and HCR radial velocity can be viewed as a proxy, we also calculate velocity texture (TVEL) and integrate it into the ECCO-V version of the algorithm. (Note that for radars that do not provide radial velocity, or when its use is inconvenient for other reasons, ECCO-V can be used with TDBZ alone.) TVEL is calculated in the same way as TDBZ. However, the base value that is subtracted from VEL is set to −20 m s−1 to ensure that no negative velocity values are present. Calculating TVEL the same way as TDBZ works well but slightly increases the TVEL values in areas with downward motions. How to best mitigate this effect will be evaluated in the future. In an additional preprocessing step, each contiguous cloud echo is slightly eroded to avoid artifacts from noise at the cloud edges. Note that velocity folding needs to be taken into account when calculating TVEL.
The concept of convectivity was introduced in ECCO-3D as a numeric measure of the convective versus stratiform nature of each grid location. It ranges from 0 to 1, where 0 represents purely stratiform and 1 represents purely convective echo. To map TDBZ and TVEL to a common convectivity field we first scale them by dividing them by 12 dBZ for TDBZ and 5 m s−1 for TVEL. These scaling values were found experimentally and will vary for different radars. The TDBZ scaling value was chosen such that the majority of the TDBZ values map to the desired range from 0 to 1, and the boundary value that differentiates convective from stratiform echo falls near 0.5. The TVEL scaling value is chosen to lead to high scaled TVEL values, as this enables the algorithm to identify small-scale convective features embedded in stratiform regions. Since there is no objective truth that these scaling values can be tuned to, they need to be carefully adjusted by visual judgment to meet the specific needs of the intended application of the resulting echo-type field.
An example of DBZ and VEL sampled from 1230 to 1251 UTC 27 September 2019, off the coast of Costa Rica during the Organization of Tropical East Pacific Convection (OTREC) field campaign (Fuchs‐Stone et al. 2020) is shown in Figs. 2a and 2b. In this case HCR sampled a mid- to high-level stratiform cloud with new convective cells forming underneath. TDBZ (Fig. 2c), scaled by 12, shows the desired low values in stratiform clouds and high values in convective clouds. TVEL (Fig. 2d), scaled by 5, generally leans toward the high end and highlights small-scale convective features.
(a) Reflectivity, (b) radial velocity, (c) scaled TDBZ, (d) scaled TVEL, and (e) convectivity from HCR data sampled from 1232 to 1251 UTC 27 Sep 2019 during the OTREC field campaign off the west coast of Costa Rica. The altitude of the aircraft is shown as a black line in (a).
Citation: Journal of Atmospheric and Oceanic Technology 39, 11; 10.1175/JTECH-D-22-0019.1
We calculate convectivity (Fig. 2e) by multiplying the scaled TDBZ and TVEL fields with each other, and setting values greater than 1 to 1. Multiplying the two texture fields has the advantage that high values are retained in regions where one of the fields is high and the other field has at least medium-high values, but not in regions where one field is high but the other is low. The result can be seen in the stratiform cloud between 1234 and 1238 UTC (Fig. 2). Reflectivity is strong and also quite variable between 7- and 9-km altitude, leading to scaled TDBZ values of 0.6, and higher in isolated small-scale regions. TVEL values are particularly high in a small region of strong up- and downdrafts around 1235:30 UTC at 9-km altitude, but also along the cloud edges. The combination of TDBZ and TVEL into convectivity neither retains the high-TDBZ regions that are purely due to high reflectivity values in the stratiform cloud, nor the high-TVEL regions at the cloud edges, as they are canceled out by the respective other field. The only regions retained are those with both high TDBZ and TVEL values with the strong up- and downdrafts, mentioned above. The combination of the two fields leads to the desired result, that convectivity is high in the developing convective cells and mostly low in the stratiform cloud, but small-scale embedded regions of high convectivity are identified within the stratiform cloud (Fig. 2e). If radial velocity is not available, convectivity can be calculated from TDBZ alone by omitting the multiplication step. However, we found that combining both fields leads to better accuracy and better detection of small-scale convective features.
b. Echo-type classification
A basic classification into convective, mixed, and stratiform echo is achieved by simply setting thresholds on the convectivity field. Following the ECCO-3D method, we set a threshold of 0.4 to separate stratiform from mixed echo and a threshold of 0.5 to separate mixed from convective echo. As mentioned before, the scaling of TDBZ and TVEL was specifically adjusted to separate convective from stratiform echo in the middle of the convectivity range. We perform a few postprocessing steps, which are unique to ECCO-V, to improve the classification. First, we remove mixed and convective features that are smaller than a certain number of contiguous grid points (500 for HCR). The second postprocessing step is specific to stratiform precipitation. While in cloud echo above the melting layer, stratiform echo in general presents as homogeneous horizontal layers, which is not necessarily the case for stratiform precipitation below the melting layer. The strength of precipitation can vary significantly over short horizontal distances, which results in vertically oriented reflectivity structures that could be misclassified as convective echo by the algorithm. To identify these potential misclassifications we check each mixed and convective feature and find those that fulfill both of the following criteria: 1) they are mostly located below the melting layer and 2) they are located below a thick layer of stratiform echo aloft. Echo features that fulfill these criteria are set to stratiform. For the remaining mixed and convective contiguous features, we perform morphological dilation followed by morphological closing (Gonzalez et al. 2020) to join features that are in close proximity. During these operations we make sure not to dilate into stratiform regions that are not vertically connected. An example of the basic classification resulting from the thresholding and the postprocessing steps is shown in Fig. 3a for the case of Fig. 2. The algorithm correctly identifies both the stratiform and convective clouds but also highlights elevated small-scale convective features within the stratiform cloud.
(a) Basic echo type and (b) advanced echo type for the same case as in Fig. 2.
Citation: Journal of Atmospheric and Oceanic Technology 39, 11; 10.1175/JTECH-D-22-0019.1
For certain applications, convectivity or the basic echo-type classification may provide all needed information. However, to provide additional information on the convective or stratiform echo, we perform a secondary advanced subclassification, again following the ECCO-3D method. A flowchart for the steps in the advanced classification scheme is shown in Fig. 4. For the advanced classification, the atmosphere is divided into three regions: The low and warm region below the melting layer (which is provided in the HCR datasets; Romatschke et al. 2021), the midregion above the melting layer but below the divergence level, and the high region above the divergence level. Following ECCO-3D, we set the divergence level to coincide with the −25°C isotherm. Stratiform echo is divided into low, mid-, and high stratiform echo accordingly. Convective features with echo tops below the melting layer are classified as shallow convective echoes. If they extend above the melting layer but top out below the divergence level they are classified as midconvective, and if they extend above the divergence level they are classified as deep convective echoes. Convective features that do not extend downward to close to the surface are classified as elevated convective echoes, with one exception: If the convective feature is located directly above an area where the radar signal is completely attenuated (marked by a quality flag of extinct; Romatschke et al. 2021), it is assumed to reach all the way to the surface and is classified accordingly. Especially in heavy convection, echo extinction at W band is quite frequent, e.g., under the deep convective feature at 1247 UTC in Figs. 2 and 3.
Flowcharts (a) for the classification of convectivity into the basic echo types and for the advanced echo-type classification of (b) stratiform echo and (c) convective features.
Citation: Journal of Atmospheric and Oceanic Technology 39, 11; 10.1175/JTECH-D-22-0019.1
When the aircraft penetrates a cloud and HCR only samples a part of the cloud as it points either nadir or zenith, it is sometimes not possible to decide to which of the convective categories a convective feature belongs. For example, the feature top may be unknown if HCR is pointing nadir. In such cases the feature retains its convective classification from the basic partitioning without the addition of an advanced category. This limitation does not apply to spaceborne or ground-based applications as such radars generally sample whole clouds. The final classification with all its subcategories is shown in Fig. 3b. The algorithm labels convective cells in their very early stages as shallow (1245:30 and 1248:40 UTC), growing convective cells as mid- (1242–1244 UTC), and mature convective cells as deep (1247–1248 and 1250–1251 UTC). The stratiform cloud is divided into a mid- and a high part and the small-scale embedded convective features are labeled as elevated convective echoes.
As a final step we collapse the final classification into the traditional classification used by previous algorithms where each vertical column is assigned a single classification. A useful one-dimensional (1D) classification along the time axis is created by assigning classification values in the 2D classification in a thoughtful way. Since most users will likely be interested in whether convective echo exists anywhere in the vertical column, we give convective echo precedence over stratiform echo. We therefore assign the classification flag values in the 2D classification in the following ascending order, which represents a prioritization from low to high: low, mid-, and high stratiform echo, mixed echo, and elevated, shallow, mid-, and deep convective echo. We take the maximum value in each vertical column to derive the 1D classification shown as a colored line in Fig. 3b. (This prioritization can easily be changed based on user needs.)
4. Results
a. HCR field campaigns
As mentioned earlier (section 3), the algorithm was developed on HCR observations and extensively tested and adjusted to provide realistic results, as determined by a human expert, for observations from all of the major HCR field campaigns to date. Since, to our knowledge, this is the first algorithm for vertically pointing radars which classifies echoes in regions with all hydrometeor types at each vertical level, there is no direct way to evaluate its performance. The original ECCO-3D method was thoroughly statistically evaluated and the results compared well with previous algorithms and lightning data. We expect ECCO-V to perform similarly. However, as with all echo-type classification algorithms, some misclassifications are unavoidable. As Powell et al. (2016) point out, it is “probably not a good idea to use any rain-type classification algorithm … to confidently classify any particular single echo. Instead, one can apply the algorithm to large datasets and determine differences between the various categories of convection on average.”
In this section we show two examples of the ECCO-V results from two very different field campaigns, the Southern Ocean Clouds Radiation Aerosol Transport Experimental Study (SOCRATES) and OTREC. The SOCRATES field campaign took place in January and February 2018, and consisted of 15 research flights over the Southern Ocean. It was designed to gather observations of the vertical distribution and properties of clouds and aerosols (McFarquhar et al. 2021). The clouds sampled during SOCRATES were mostly stratiform in nature. The example in Fig. 5, which was collected from 0109 to 0153 UTC 29 January 2018, shows a mid- to high-altitude stratiform cloud layer and a low- to midlevel precipitating cloud deck with embedded convective cells. The algorithm correctly identifies the midconvective cells embedded in the lower cloud layer. Toward the end of the time frame shown, the aircraft penetrated the top of the convective cells, and it was not possible to classify them into subcategories. They keep the basic convective classification.
(a) Reflectivity, (b) radial velocity, (c) convectivity, and (d) advanced echo type from HCR data sampled from 0109 to 0153 UTC 29 Jan 2018, during the SOCRATES field campaign over the Southern Ocean, south of Tasmania, ∼2000 km north of Antarctica. The altitude of the aircraft is shown as a black line in (a).
Citation: Journal of Atmospheric and Oceanic Technology 39, 11; 10.1175/JTECH-D-22-0019.1
The OTREC field campaign, which took place from August to October 2019, over the tropical east Pacific and the Caribbean Ocean, occurred in a very different environment. The sampled clouds and precipitation were associated with tropical convective systems of all scales and stages of development. The example shown in Fig. 6 shows a cross section through a mature thunderstorm (1738–1748 UTC) with a developing anvil cloud, newly developing thunderstorms in their early (1754 UTC) and middle stages (1750 UTC), and anvil clouds from nearby thunderstorms (1729–1738 UTC). ECCO-V identifies the anvil clouds as mid- to high stratiform. It also captures the convective updraft region within the mature thunderstorm and labels it as a deep convective system. The part of the mature thunderstorm, which already developed a clear melting layer, is classified as stratiform (1744–1748 UTC). Small-scale embedded convective echoes aloft are classified as elevated convective features. The classification of the newly developing cells as mid- and deep reflect their stages of development.
As in Fig. 5, but for data collected during OTREC from 1729 to 1756 UTC 11 Aug 2019, off the east coast of Panama.
Citation: Journal of Atmospheric and Oceanic Technology 39, 11; 10.1175/JTECH-D-22-0019.1
b. CloudSat
To tune ECCO-V for the spaceborne CloudSat CPR, input variables such as the length of the time segment, the scaling parameters, and the intensity of the morphological operations had to be adjusted. The freezing-level altitude was provided in the CloudSat data and the divergence-level altitude was set to the freezing-level altitude plus 4 km. The convectivity is based on reflectivity only as only TDBZ is used.
ECCO-V was applied to all CloudSat swaths from July and August 2015. We plotted the ECCO-V vertically resolved echo type, and both the CloudSat convective–stratiform echo-type flag (Haynes 2018) and the ECCO-V 1D classification, to investigate the similarities and differences between the two algorithms. The CloudSat echo flag is a 1D variable on the time dimension and provides labels of convective, stratiform, or shallow for each ray.
An example of such a comparison plot (Fig. 7) is used to summarize our findings. It shows a storm in its mature stage of development with an active deep convective cell (0307:50–0308:05 UTC) and a large stratiform region with anvil. New convective cells develop on both sides of the storm (0306:45–0307:00 and 0308:15–0308:32 UTC), partly underneath the anvil. This scene was captured from 0306:45 to 0308:32 UTC 4 July 2015, over the Solomon Sea. Both algorithms identify the main stratiform and convective characteristics but differ in the details, as they were designed for different purposes. The CloudSat algorithm, which operates on a ray-by-ray basis, shows more horizontal variability than ECCO-V, which is designed to join convective features that are close together. These different approaches are evident in the identification results for the convective cell, which is spread out horizontally by ECCO-V (1D echo type, Fig. 7c), but shows interspersed stratiform echo in the CloudSat classification (Fig. 7d). ECCO-V, which is tuned to find only significant convective features, labels some shallow echo with very low reflectivity as stratiform (0308:15–0308:25 UTC), whereas these are labeled as shallow by the CloudSat algorithm which is focused on classifying all surface precipitation. Like HCR, CloudSat is a 94 GHz radar and therefore experiences severe attenuation, sometimes to complete signal extinction (0307:00–0307:05 UTC). Since the CloudSat algorithm does not identify extinct echo, it has a tendency to misidentify stratiform echo as convective in cases where the echo extinction reaches all the way to the melting layer (0307:10–0307:15 UTC and around 0307:30 UTC). This behavior is expected as the CloudSat algorithm relies heavily on the existence of a bright band (Haynes 2018).
(a) Reflectivity, (b) convectivity, and (c) ECCO-V, and (d) CloudSat echo type from CloudSat CPR data sampled from 0306:45 to 0308:32 UTC 4 Jul 2015 over the Solomon Sea.
Citation: Journal of Atmospheric and Oceanic Technology 39, 11; 10.1175/JTECH-D-22-0019.1
c. MMCR
ECCO-V was applied to one example case from the ground-based MMCR (Fig. 8). The input parameters were tuned specifically for this case and would likely require additional adjustments before ECCO-V could be deployed on a routine basis for this radar. The MMCR observed a mostly stratiform cloud, which started precipitating as it passed over the radar at its site in Oklahoma, and a developing convective cell between 1345 and 1435 UTC 17 August 2008 (Fig. 8). The developing cell (1420–1430 UTC) was classified as midconvective and the stratiform cloud as midstratiform, while the stratiform precipitation was classified as low stratiform. The cloud above the precipitation showed elevated convective features. The robustness of the algorithm is demonstrated by this case. It performed well in conditions where a cloud is not captured at one specific stage, as is the case for air- and spaceborne radars, but in conditions where cloud and precipitation change and develop while they are sampled by the ground-based radar over a longer period of time.
(a) Reflectivity, (b) convectivity, and (c) echo type from data collected by MMCR from 1345 to 1435 UTC 17 Aug 2008, in Oklahoma.
Citation: Journal of Atmospheric and Oceanic Technology 39, 11; 10.1175/JTECH-D-22-0019.1
5. Conclusions
The ECCO-3D method for convective–stratiform partitioning introduced by Dixon and Romatschke (2022) is adapted for vertically pointing radars (ECCO-V). Observations from four field campaigns collected by the airborne HIAPER Cloud Radar (HCR) are used to develop an algorithm that differentiates between convective and stratiform radar echoes not only on a vertical column by column basis, but also provides a vertically resolved classification field. ECCO-V is based on the calculation of reflectivity and velocity texture fields which provide information on the horizontal homogeneity and heterogeneity of the observations. The texture fields are combined into a convectivity field, which provides a numeric measure of how convective or stratiform each sample point is. Convectivity can be used directly in suitable applications or it can be thresholded into the more traditional convective, stratiform, and in our case also mixed, radar-echo categories. In a last step, the categories are refined to differentiate between low, mid-, and high stratiform echoes, and shallow, mid-, deep, and elevated convective echoes, and a mixed-echo category, based on the melting-layer and the divergence-level altitudes, which are derived from temperature.
ECCO-V performs well and identifies not only isolated convective or stratiform clouds, but is particularly skilled in finding embedded convective features. For example, it differentiates between highly convective updrafts and aging stratiform regions within the same thunderstorm. It also finds newly developing convective cells underneath or even penetrating an elevated stratiform cloud layer. Elevated convective features, which are often embedded in stratiform clouds, are also identified. These capabilities highlight the importance of vertically resolved convective–stratiform echo-type partitioning, in comparison to a simple 1D classification, which does not fully describe the complexity of multilayered cloud scenes.
ECCO-V is robust and was extensively tested in very different environments ranging from the tropics to the Southern Ocean. Its simplicity and the fact that the only required input fields are the direct radar measurements of reflectivity (and optional radial velocity) makes it highly adaptable and it was easily adjusted for use in spaceborne and ground-based vertically pointing radar systems. In addition to its portability to different types of radars, it can also be adjusted for different uses and research requirements. The calculation of convectivity allows for highlighting of different characteristics of cloud and precipitation systems by adjusting the thresholding accordingly. ECCO-V is a versatile tool for cloud and precipitation research and we are confident that the convectivity and convective–stratiform echo-type fields provided in the HCR datasets will lead to new insights into the mechanisms that govern cloud and precipitation formation and development.
Acknowledgments.
This material is based upon work supported by the National Center for Atmospheric Research, which is a major facility sponsored by the National Science Foundation under Cooperative Agreement 1852977. We thank all members of the HCR team for the flawless operation of the radar during all HCR field campaigns. We thank John Hubbert and Brad Klotz for valuable comments on the manuscript.
Data availability statement.
HCR data from four major field campaigns is available in the EOL Field Data Archive https://data.eol.ucar.edu. The data for the CSET field campaign are available at https://doi.org/10.5065/D6CJ8BV7 (NCAR/EOL HCR Team 2022a), the SOCRATES data at https://doi.org/10.5065/D68914PH (NCAR/EOL HCR Team 2022b), the data for OTREC at https://doi.org/10.26023/V9DJ-7T9J-PE0S (NCAR/EOL HCR Team 2022c), and the SPICULE data at https://doi.org/10.26023/PGGK-MC4T-K70F (NCAR/EOL HCR Team 2022d). CloudSat data are available at https://www.cloudsat.cira.colostate.edu/. We use radar reflectivity from the 2B-GEOPROF product (Marchand et al. 2008), and the convective–stratiform flag and freezing level from the 2C-PRECIP-COLUMN product (Haynes 2018). MMCR data are available at https://doi.org/10.5439/1025228 [ARM (2003) user facility]. The ECCO-V algorithm adapted for the CloudSat CPR is available at https://github.com/NCAR/cloudSat_ecco-v.
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