Two-dimensional video disdrometer (2DVD) data were analyzed from two equatorial Indian (Gan) and west Pacific Ocean (Manus) islands where precipitation is primarily organized by the intertropical convergence zone and the Madden–Julian oscillation (MJO). The 18 (3.5) months of 2DVD data from Manus (Gan) Island show that 1) the two sites have similar drop size distribution (DSD) spectra of liquid water content, median diameter, rain rate R, radar reflectivity z, normalized gamma number concentration Nw, and other integral rain parameters; 2) there is a robust Nw-based separation between convective (C) and stratiform (S) DSDs at both sites that produces consistent separation in other parameter spaces. The 2DVD data indicate an equatorial, maritime average C/S rainfall accumulation fraction (frequency) of 81/19 (41/59) at these locations. It is hypothesized that convective fraction and frequency estimates are slightly higher than previous radar-based studies, because the ubiquitous weak, shallow convection (<10 mm h−1) characteristic of the tropical warm pool is properly resolved by this high-resolution DSD dataset and identification method. This type of convection accounted for about 30% of all rain events and 15% of total rain volume. These rain statistics were reproduced when newly derived C/S R(z) equations were applied to 2DVD-simulated reflectivity. However, the benefits of using separate C/S R(z) equations are only realizable when C/S partitioning properly classifies each rain type. A single R(z) relationship fit to all 2DVD data yielded accurate total rainfall amounts but overestimated (underestimated) the stratiform (convective) rain fraction by ±10% and overestimated (underestimated) stratiform (convective) rain accumulation by +50% (−15%).

Observations from 1 km beneath to 25 km above the sea surface reveal the complex interactions in Indian Ocean westerly wind bursts associated with the Madden-Julian oscillation.

Understanding drop size distribution (DSD) variability has important implications for remote sensing and numerical modeling applications. Twelve disdrometer datasets across three latitude bands are analyzed in this study, spanning a broad range of precipitation regimes: light rain, orographic, deep convective, organized midlatitude, and tropical oceanic. Principal component analysis (PCA) is used to reveal comprehensive modes of global DSD spatial and temporal variability. Although the locations contain different distributions of individual DSD parameters, all locations are found to have the same modes of variability. Based on PCA, six groups of points with unique DSD characteristics emerge. The physical processes that underpin these groups are revealed through supporting radar observations. Group 1 (group 2) is characterized by high (low) liquid water content (LWC), broad (narrow) distribution widths, and large (small) median drop diameters D0. Radar analysis identifies group 1 (group 2) as convective (stratiform) rainfall. Group 3 is characterized by weak, shallow radar echoes and large concentrations of small drops, indicative of warm rain showers. Group 4 identifies heavy stratiform precipitation. The low latitudes exhibit distinct bimodal distributions of the normalized intercept parameter N w, LWC, and D0 and are found to have a clustering of points (group 5) with high rain rates, large N w, and moderate D0, a signature of robust warm rain processes. A distinct group associated with ice-based convection (group 6) emerges in the midlatitudes. Although all locations exhibit the same covariance of parameters associated with these groups, it is likely that the physical processes responsible for shaping the DSDs vary as a function of location.

Stratification of the upper few meters of the ocean limits the penetration depth of wind mixing and the vertical distribution of atmospheric fluxes. Significant density stratification at depths ≤ 5 m was observed in 38% of a 2‐month data set from the central Indian Ocean collected during the DYNAMO experiment (Dynamics of the MJO, Madden‐Julian Oscillation). Diurnal warm layers (DWLs) formed by solar heating populated 30% of the data set and rain layers (RLs) populated 16%. Combined contributions from rain and insolation formed RL‐DWLs in 9% of the data set. RLs were detected at values of U10 up to 9.8 m s−1, while DWLs were only detected at U10 < 7.6 m s−1 (99th percentile values), symptomatic of the greater buoyancy flux provided by moderate to high rain rate compared to insolation. From the ocean friction velocity, u*w, and surface buoyancy flux, B, we derived estimates of htruêS, stable layer depth, and UtruêS, the maximum U10 for which stratification should persist at htruêS for fixed B. These estimates predicted (1) 36 out of 44 observed stratification events (88% success rate) and (2) the wind limits of these events, which are considered to be the 99th percentile values of U10). This suggests a means to determine the presence of ocean stable layers at depths ≤ 5 m from U10 and B. Near‐surface stratification varied throughout two Madden‐Julian Oscillation (MJO) cycles. In suppressed MJO periods, (U10 ≤ 8 m s−1 with strong insolation), RLs and RL‐DWLs were rare while DWLs occurred daily. During disturbed and active MJO periods, (U10 ≤ 8 m s−1 with increased rain and cloudiness), multiple RLs and RL‐DWLs formed per day and DWLs became less common. When westerly wind bursts occurred, (U10 = 7–17 m s−1 with steady rain), RLs formed infrequently and DWLs were not detected.

Dual-polarization radar rainfall estimation relationships have been extensively tested in continental and subtropical coastal rain regimes, with little testing over tropical oceans where the majority of rain on Earth occurs. A 1.5-yr Indo-Pacific warm pool disdrometer dataset was used to quantify the impacts of tropical oceanic drop-size distribution (DSD) variability on dual-polarization radar variables and their resulting utility for rainfall estimation. Variables that were analyzed include differential reflectivity Zdr; specific differential phase Kdp; reflectivity Zh; and specific attenuation Ah. When compared with continental or coastal convection, tropical oceanic Zdr and Kdp values were more often of low magnitude (&lt;0.5 dB, &lt;0.3° km−1) and Zdr was lower for a given Kdp or Zh, consistent with observations of tropical oceanic DSDs being dominated by numerous, small, less-oblate drops. New X-, C-, and S-band R estimators were derived: R(Kdp), R(Ah), R(Kdp, ζdr), R(z, ζdr), and R(Ah, ζdr), which use linear versions of Zdr and Zh, namely ζdr and z. Except for R(Kdp), convective/stratiform partitioning was unnecessary for these estimators. All dual-polarization estimators outperformed updated R(z) estimators derived from the same dataset. The best-performing estimator was R(Kdp, ζdr), followed by R(Ah, ζdr) and R(z, ζdr). The R error was further reduced in an updated blended algorithm choosing between R(z), R(z, ζdr), R(Kdp), and R(Kdp, ζdr) depending on Zdr &gt; 0.25 dB and Kdp &gt; 0.3° km−1 thresholds. Because of these thresholds and the lack of hail, R(Kdp) was never used. At all wavelengths, R(z) was still needed 43% of the time during light rain (R &lt; 5 mm h−1, Zdr &lt; 0.25 dB), composing 7% of the total rain volume. As wavelength decreased, R(Kdp, ζdr) was used more often, R(z, ζdr) was used less often, and the blended algorithm became increasingly more accurate than R(z).

The purpose of this study is to demonstrate the use of polarimetric observations in a radar-based winter hydrometeor classification algorithm. This is accomplished by deriving bulk electromagnetic scattering properties of stratiform, cold-season rain, freezing rain, sleet, dry aggregated snowflakes, dendritic snow crystals, and platelike snow crystals at X-, C-, and S-band wavelengths based on microphysical theory and previous observational studies. These results are then used to define the expected value ranges, or membership beta functions, of a simple fuzzy-logic hydrometeor classification algorithm. To test the algorithm's validity and robustness, polarimetric radar data and algorithm output from four unique winter storms are investigated alongside surface observations and thermodynamic soundings. This analysis supports that the algorithm is able to realistically discern regions dominated by wet snow, aggregates, plates, dendrites, and other small ice crystals based solely on polarimetric data, with guidance from a melting-level detection algorithm but without external temperature information. Temperature is still used to distinguish rain from freezing rain or sleet below the radar-detected melting level. After appropriate data quality control, little modification of the algorithm was required to produce physically reasonable results on four different radar platforms at X, C, and S bands. However, classification seemed more robust at shorter wavelengths because the specific differential phase is heavily weighted in ice crystal classification decisions. It is suggested that parts, or all, of this algorithm could be applicable in both operational and research settings.

Abstract. The Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign (ATOMIC) took place from 7 January to 11 July 2020 in the tropical North Atlantic between the eastern edge of Barbados and 51∘ W, the longitude of the Northwest Tropical Atlantic Station (NTAS) mooring. Measurements were made to gather information on shallow atmospheric convection, the effects of aerosols and clouds on the ocean surface energy budget, and mesoscale oceanic processes. Multiple platforms were deployed during ATOMIC including the NOAA RV Ronald H. Brown (RHB) (7 January to 13 February) and WP-3D Orion (P-3) aircraft (17 January to 10 February), the University of Colorado's Robust Autonomous Aerial Vehicle-Endurant Nimble (RAAVEN) uncrewed aerial system (UAS) (24 January to 15 February), NOAA- and NASA-sponsored Saildrones (12 January to 11 July), and Surface Velocity Program Salinity (SVPS) surface ocean drifters (23 January to 29 April). The RV Ronald H. Brown conducted in situ and remote sensing measurements of oceanic and atmospheric properties with an emphasis on mesoscale oceanic–atmospheric coupling and aerosol–cloud interactions. In addition, the ship served as a launching pad for Wave Gliders, Surface Wave Instrument Floats with Tracking (SWIFTs), and radiosondes. Details of measurements made from the RV Ronald H. Brown, ship-deployed assets, and other platforms closely coordinated with the ship during ATOMIC are provided here. These platforms include Saildrone 1064 and the RAAVEN UAS as well as the Barbados Cloud Observatory (BCO) and Barbados Atmospheric Chemistry Observatory (BACO). Inter-platform comparisons are presented to assess consistency in the data sets. Data sets from the RV Ronald H. Brown and deployed assets have been quality controlled and are publicly available at NOAA's National Centers for Environmental Information (NCEI) data archive (https://www.ncei.noaa.gov/archive/accession/ATOMIC-2020, last access: 2 April 2021). Point-of-contact information and links to individual data sets with digital object identifiers (DOIs) are provided herein.

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