OceanRAIN—the Ocean Rainfall And Ice-phase precipitation measurement Network—provides in-situ along-track shipboard data of precipitation, evaporation and the resulting freshwater flux at 1-min resolution over the global oceans from June 2010 to April 2017. More than 6.83 million minutes with 75 parameters from 8 ships cover all routinely measured atmospheric and oceanographic state variables along with those required to derive the turbulent heat fluxes. The precipitation parameter is based on measurements of the optical disdrometer ODM470 specifically designed for all-weather shipboard operations. The rain, snow and mixed-phase precipitation occurrence, intensity and accumulation are derived from particle size distributions. Additionally, microphysical parameters and radar-related parameters are provided. Addressing the need for high-quality in-situ precipitation data over the global oceans, OceanRAIN-1.0 is the first comprehensive along-track in-situ water cycle surface reference dataset for satellite product validation and retrieval calibration of the GPM (Global Precipitation Measurement) era, to improve the representation of precipitation and air-sea interactions in re-analyses and models, and to improve understanding of water cycle processes over the global oceans.
observation design • time series design • source-based data analysis objective
water cycle • hydrological precipitation process
data acquisition system
temporal_interval • DataTypes
Atlantic Ocean • Pacific Ocean • Southern Ocean • hydrological process
Machine-accessible metadata file describing the reported data (ISA-Tab format)
Background & Summary
Global ocean water cycle monitoring is essential for a successful understanding of the climate system1–4. The net gain (precipitation) or loss (evaporation) of water through the ocean surface yields the freshwater flux, linking the global water cycle to the energy budget through latent heat exchange5. The freshwater flux couples the ocean to the atmosphere, driving oceanic and atmospheric circulations with impacts on sea surface temperature and salinity6,7. Several factors complicate the measurement of precipitation such as its intermittent nature, inhomogeneous spatial distribution, intensity variations and phase changes, as well as technical detection limits for very light precipitation. Despite its large uncertainties8,9, precipitation is likely the single most essential climate variable (ECV) to characterize4,3,10.
Recent satellite remote sensing products provide unprecedented spatio-temporal coverage of evaporation, fostered by the SeaFlux community11 under the auspices of the World Climate Research Programme (WCRP), and precipitation through the International Precipitation Working Group9,12,13 (IPWG), co-sponsored by the World Meteorological Organization (WMO). The TRMM14 (Tropical Rainfall Measurement Mission), CloudSat15,16 and Global Precipitation Measurement17 (GPM) satellite missions, aided by their spaceborne precipitation radars TRMM-PR, CloudSat-CPR and GPM-DPR boosted the emergence of precipitation products18–24. The Integrated Multi-satellitE Retrieval for GPM25 (IMERG) retrievals discriminate between rain and snow and feature increased sensitivity to light rain. However, all these products exhibit sensitivity issues as well as large differences among each other, especially for light precipitation and high-latitude cold-season precipitation26–29.
Therefore, thorough evaluation of the associated errors, biases and uncertainties to improve the satellite retrievals requires a comprehensive and consistently derived in-situ surface-reference dataset over the global oceans with special emphasis on the precipitation parameter4,30.
The hitherto lack of such a validation dataset mainly arose from the unavailability of suitable instrumentation. Gauge-type devices remained the principal source of in-situ precipitation measurement over ocean on ships8,31–33 and buoy networks34–36. They serve as an input to a variety of ship-based precipitation estimates31,37–41. However, gauges are generally not well suited to this task because of large wind-induced rainfall undercatch resulting from superimposed ship’s speed, surface wind speed and turbulent flow distortion around the ship superstructure42–45. Moreover, snow and mixed-phase precipitation is either blown over or clog the gauge orifice resulting in false measurements.
In addition to the rain rate provided by gauges, particle size distributions (PSDs) are required to derive microphysical rain and snowfall properties and reflectivities at different radar operating frequencies. These parameters are essential to accurately convert the radar reflectivity into a precipitation rate46,47. Such in-situ validation data for retrieval calibration48–50 can only be provided by disdrometers8,30,51,52. However, most of the existing disdrometers are not designed for all-weather shipboard operation and thus do not meet the requirements for strong turbulence, frequently varying wind directions and sea state.
Therefore, this observational gap in high-quality precipitation measurements over the ocean51,53,54 remained critical for meeting the requirements of validating an ECV30. This motivated us to develop and implement a monitoring network for in-situ precipitation, evaporation and freshwater flux to support the requirements of the international science teams of IPWG, GPM-GV (Ground Validation), SeaFlux and OceanObs11,13,17,55 and the Global Climate Observing System4. All satellite-based precipitation retrievals and products as well as re-analyses and models involving air-sea interaction would benefit from such new in-situ reference dataset8.
To bridge this important information gap, we present the surface reference dataset OceanRAIN-1.0 (Ocean Rainfall And Ice-phase precipitation measurement Network) (Data Citation 1, Data Citation 2 and Data Citation 3). OceanRAIN is the first in-situ global ocean shipboard dataset comprising 75 meteorological and oceanographic parameters including consistently derived along-track precipitation, evaporation, the resulting freshwater flux as well as surface turbulent fluxes. The precipitation parameters include rain, snow and mixed-phase precipitation occurrence, intensity and accumulation, all derived through PSDs based on automated ODM470 optical disdrometers that were specifically designed to meet all-weather shipboard requirements. All relevant microphysical precipitation parameters and reflectivities at different radar frequencies are also provided. These datasets are collected during the ongoing long-term installations and special campaigns onboard eight research vessels from June 2010 to April 2017 covering all latitudes, oceanic basins and seasons and comprise more than 6.83 million minutes including 696,740 precipitation minutes (Table 1). OceanRAIN-1.0 data is publicly available through the website http://www.oceanrain.org/ and the World Data Center for Climate (WDCC).
OceanRAIN is designed to meet the IPWG, GPM Ground Validation, SeaFlux and OceanObs requirements for high-quality in-situ water cycle reference data of precipitation, evaporation and freshwater flux measurement over the global oceans. OceanRAIN is primarily intended for validating satellite data products and retrievals and for understanding and reducing their uncertainties. The Global Climate Observing System4 calls for the development and implementation of improved methods for observing precipitation, and deriving associated products. It also strongly recommends assessing uncertainties of satellite precipitation products, using newly developed automated in-situ data over the global oceans and with special focus on high-latitudes. This demand is also supported by the recommendation of the Coordination Group for Meteorological Satellites (CGMS) to maintain existing and explore new sources of in-situ precipitation measurement in data sparse regions such as the global oceans.
Accurately simulating precipitation processes and associated statistical rainfall properties (accumulation, instantaneous intensity and frequency of rainfall) with general circulation models is paramount for hydrological applications, numerical weather prediction and climate change studies. As discussed in Stephens et al.56, changes to both the frequency and intensity of precipitation occur in climate-warming experiments. The evaluation of rainfall accumulation over seasonal or longer time-scales provides limited insights into the validity of convective processes simulated by models. Therefore, several studies have attempted to evaluate both the intensity and frequency of rainfall simulated by large-scale models, using either land-based observations57,58 or oceanic rainfall derived from satellite measurements56. These comparisons revealed that, although modelled rainfall accumulations at different temporal scales are generally reasonably accurate, general circulation models tend to overestimate the frequency and underestimate the intensity of precipitation, with different behavior of the models in different regions and latitudes56.
Oceans cover about 71% of Earth’s surface and accounts for an estimated 78% of global precipitation36. The hitherto lack of surface precipitation data36,51 hindered validation efforts because of the unavailability of a capable instrumentation, e.g. disdrometers, for shipboard operation. This clearly highlights the need for global ocean observations of precipitation in different large-scale regimes to better understand underlying causes for model discrepancies and to help improve model parameterizations of precipitation processes.
Reliable in-situ observations of rain, snow and mixed-phase, occurrence, intensity and accumulation through PSDs over all oceanic basins and seasons are critical to validate the precipitation ECV. Since 2009, the OceanRAIN project tackled the challenge involved to develop, operate and long-term maintain a network of automated disdrometer systems under all-weather conditions onboard globally operating Research Vessels (RVs) and to develop an automated data post-processing chain30.
Consequently, the optical disdrometer ODM470 initially developed by GEOMAR59 in Kiel, Germany, was selected because it was designed to meet all-weather shipboard requirements. Within OceanRAIN we tested, utilized and further developed the disdrometer into a fully automated measurement system, the ODM470 (ref. 30) built by the company Eigenbrodt GmbH & Co. KG in Königsmoor, Germany. During its development and testing period the ODM470 was already successfully used in several studies and shipboard campaigns including first validation of satellite data and the evaluation of reanalysis products60–64.
To complement the in-situ shipboard precipitation measurement, OceanRAIN also ingests and stores all routinely measured shipboard meteorological and oceanographic data streams needed to derive the evaporation parameter. With the inclusion of validated true-zero precipitation measurements, OceanRAIN is to date the only publicly available comprehensive dataset providing the full suite of water cycle parameters, including the along-track freshwater flux in a consistent framework at 1-min resolution. RVs with long-term OceanRAIN installations offer a perfect framework because they operate in all climate-relevant areas and seasons over the global remote oceans including the high-latitudes and do not circumvent routinely high-impact weather. This largely avoids the so-called fair weather bias that occurs when merchant ships or cruise liners circumvent high-impact weather along their main shipping routes. For these reasons the OceanRAIN data set comprises the entire spectrum of weather events including extreme values in remote locations.
These long-term installations are complemented by RVs providing short-term campaign data with special emphasis on water cycle analysis. We contributed to the tropical Pacific SPURS (Salinity Processes in the Upper Ocean Regional Study) campaign onboard RV Roger Revelle, and the CAPRICORN (Clouds, Aerosols, Precipitation Radiation and atmospherIc Composition Over the southeRN ocean) cruises onboard RV Investigator with a focus on GPM and CloudSat satellite validation using collocated ship underpasses (see Technical Validation section). Table 1 lists the eight ships in the OceanRAIN-1.0 fleet. All large German RVs are long-term equipped with the OceanRAIN instrumentation covering the Atlantic Ocean and also parts of the Pacific Ocean including the high latitudes and cold seasons. RV Polarstern, RV Investigator and MS The World focus on the high-latitudes with a large precipitation fraction falling as mixed-phase and snow at temperatures as low as −30 °C.
Beyond the application of satellite product validation and retrieval calibration, the OceanRAIN weather and ocean monitoring is used to develop methods to improve point-to-area representativeness analysis of precipitation65–67. Moreover, they deliver a broad spectrum of reflectivity (Z) and precipitation (R) relationships (Z-R) for different radar frequencies, to broaden our understanding of water cycle processes and precipitation microphysics over the global oceans and to evaluate re-analysis and general circulation model data.
OceanRAIN precipitation instrumentation
The backbone of the OceanRAIN project is the ODM470 optical disdrometer30,59,61,68,69. An infrared light emitting diode at 880 nm homogeneously illuminates the measuring volume of 120 mm length and 22 mm diameter. Hydrometeors passing this volume cause light extinction proportional to their cross sectional area. The detected light reduction is stored as an activation voltage70. During the integration time of 60 s all hydrometeors are counted and sorted into size bins ranging from 0.04 to 22 mm to obtain the PSD.
The OceanRAIN instrumentation is complemented by a cup anemometer to measure relative wind speed and a precipitation detector (IRSS88). The smallest particles detectable by the IRSS88 are of 0.39 mm diameter and therefore the same as the ODM470. The IRSS88 activates the disdrometer when precipitation occurs. This helps to increase the lifetime of the disdrometer optics and strongly reduces the occasional risk of artificial signals caused by vibration of the instrument due to sea state, ice-breaking activity, gusty winds or the ship’s engine. Further artificial signals due to cleaning of the optics and erroneous signals caused by birds or insects are also avoided by this technique. The OceanRAIN instruments are installed as high up as possible, preferably on the ship's mast and in front of the ship's funnel, to minimize the exposure to sea spray, wave water and soot. OceanRAIN records temporally discontinuous precipitation data but also includes the true-zero precipitation information. The no-precipitation signal carries important information for the analysis of precipitation frequency of occurrence, false alarm statistics as well as retrieval behavior analysis in satellite-derived precipitation products. A detailed description of the instrumentation and its measurement principles is provided in Klepp30.
Disdrometer calibration is performed before and after shipboard operation whenever the ships are accessible during port or maintenance periods. The twofold procedure first comprises of a lab hardware calibration of the optical axis and the adjustment of the reference voltage using steel ball bearings of increasing size. This is followed by an outdoor test site calibration using real rainfall events in wind speed conditions below 5 m s−1 and a reference rain gauge (ANS410) for accumulation comparison71. Both instruments typically differ in the order of 2% rain accumulation30. The calibration drift after shipboard operation is negligible in most cases because the reference voltage is continuously checked and adjusted during the cruises if necessary. Underway lens cleaning is required in two-month intervals on average as indicated by a reference voltage drop towards a quality check threshold of 3 Volt.
The ODM470 is specifically designed and further developed for shipboard operation under all-weather conditions. The main advantages of the ODM470 system over other existing disdrometers can be specified in five points, namely (1) the measurement volume of the disdrometer has a cylindrical shape causing precipitation particles to be independent of their incidence angle, (2) the measurement volume is always kept perpendicular to the local wind direction. This is achieved by a wind vane that pivots the instrument around a vertical axis. Consequently, local up- and downdrafts as well as turbulence induced by the ships superstructure cause a minimal impact on the measurement of the particle size distribution, (3) the high-resolution optical unit allows discriminating hydrometeors into 128 size bins with an logarithmically increased resolution towards smaller size bins, (4) an automated precipitation phase detection algorithm allows to process particle size distributions using either the rainfall or snowfall algorithm, and (5) the instrument is fully automated, robust and requires minimal maintenance during operation. Thus, it is ideally suited for long-term monitoring of precipitation over the global oceans in all-weather conditions.
The instruments successfully performed during snowfall at −30 °C in the Southern Ocean close to Antarctica, during torrential rainfall in the inner tropics with up to 367 mm h−1 rainfall and during severe sea states in mid-latitude storms.
The data acquisition and data processing chain to derive water cycle and precipitation microphysical parameters is visualized in the flow chart of Fig. 1. Four universal time coordinate (UTC) time-synchronized data streams, using a joint IP time server, are recorded separately onboard the ships and are ingested into the OceanRAIN database. These are the temporally continuous shipboard navigational data (NAV), the automated underway surface atmospheric and oceanographic data (MET), the three-hourly manual synoptic weather observations (SYN) as well as the temporally discontinuous OceanRAIN ODM470 precipitation raw data (ODM) and metadata protocols on special weather and instrument conditions (META).
The NAV and MET files for all five German RVs (Table 1) are freely available from the DSHIP data center (http://dship.bsh.de/) of the Federal Maritime and Hydrographic Agency. For the Australian RV Investigator they are provided by CSIRO (Commonwealth Scientific and Industrial Research Organisation) and the Marine National Facility (MNF). The data from the American RV Roger Revelle is made available by University of Washington in Seattle. The luxury cruise liner MS The World supplies a limited set of meteorological data directly through ship contacts. Most of the ships measure air and dew-point temperature, bulk water temperature, relative humidity, air pressure, relative and true wind speed and direction, global radiation, visibility, ceiling, salinity and rain gauge data. The World Meteorological Organization (WMO) present weather (ww) and past weather (W1 and W2) codes including precipitation type and intensity observations (SYN) are additionally provided during daylight operation through the manned weather observatories onboard RV Polarstern and RV Meteor maintained by the German Weather Service. The synoptic code list is provided in Petty26.
The ODM files contain the temporally discontinuous PSDs in two raw data levels. During the integration time of 1 min, each particle occurrence is logged together with its properties into a binary file. From this 1-min resolution, data entries are automatically produced that contain the number of particles per bin size. This ODM raw data is stored into daily files that are designedly empty if no precipitation occurred. To further ensure the validity of true-zero minutes during post-processing, hourly system messages are written into log files to ensure the error-free operation of the system.
META on special weather and sea state is essential when monitoring precipitation to identify the reasons for instrument problems, outages or unusual spikes in the data. Therefore, within OceanRAIN, contacts were established with ship personnel or onboard weather observatories to log special weather and instrument occurrences. Problems during the ODM470 operation are reported via email. Consequently, data outages can be minimized by effective troubleshooting. This timely communication effectively speeds up the shipping of spare parts if required. The ODM data is transferred in delay-mode during port times.
Figure 2 provides an overview of the data recorded and processed for OceanRAIN version 1.0. The geographical distribution of all eight RVs (a) is color-coded for seasons (b), years (c) and precipitation phase occurrence including true-zero precipitation information (d). The dataset comprises a total of more than 6.83 million minutes of data with 696,740 min containing rainfall, snowfall or mixed-phase precipitation.
The NAV, MET and SYN data files are delivered in a variety of non-uniform formats, file types and differ in the number and units of the measured and observed parameters. Thus, the first step in the OceanRAIN post-processing chain is to homogenize each incoming data record into a common interim format including uniform missing values. Values in knots or feet units are converted into metric units. Additionally, float and integer values are unified. From the NAV data files the latitude data is homogenized to degree North ranging from −90° to +90° and longitude in degree East ranging from −180° to +180°. Additionally, the ship’s heading is stored. Thereafter, all four data streams are collocated into a navigated 1-min resolution match-up database using the UTC time coordinate. Additional time coordinates are calculated to obtain local date and time, minute of the day, the Unix epoch time stamp in seconds since 01 Jan 1970, 00 UTC and the Julian date since 01 Jan 1994, 00 UTC.
During port times, parameters should be handled with care because the logging systems may be under maintenance, switched off or data might be unreliable due to heavy maintenance activity on the ship’s superstructure. Therefore, port times receive flag value 5 in the dataset (Table 2).
Cascading automated and visual inspection quality controls of the navigated match-up database throughout the OceanRAIN post-processing chain are of major importance because the data ingested is direct instrument output with no initial quality screening.
The NAV data is inspected for the existence of date and time recorded in UTC, latitude, longitude and ship heading. Non-sequential, missing or corrupted data is corrected where possible or flagged missing otherwise. Duplicate time steps are removed. The MET data is automatically inspected for out-of-range values following a protocol described in Klepp30. Erroneous data blocks are rigorously excluded from the data records using missing values while obvious errors (e.g. 0.00 hPa for air pressure) are replaced by interpolated estimates using previous and next minute data where possible. This is possible for all spatio-temporally homogeneous parameters (e.g. water temperature or navigation data) but not for highly variable and intermittent parameters (e.g. precipitation). Suspicious data is flagged and accepted or rejected using a visual screening procedure. This ensures that derived parameters (e.g. evaporation) that rely on a number of input parameters are only calculated once the input parameters successfully passed the quality control test. The SYN data is flagged for precipitation occurrences and visually compared to match the ODM precipitation occurrence. The ODM data is automatically inspected and visually corrected for unrealistically high single-minute spikes. Such occurrences are however not entirely deleted from the database. For traceability, they can be tracked by their non-zero number of particles and bins and their theoretical rain and snowfall rate. In the final precipitation rate assignment they obtain a true-zero value. The automatically derived precipitation phase probability is further inspected to agree with the derived theoretical rainfall and snowfall rate (see Precipitation module) to avoid unrealistic minute-to-minute variations in the precipitation record when mixed-phase precipitation is likely to be present. Details on these production steps are provided in the precipitation module section below. Again, any changes made can be tracked by the theoretical rainfall and snowfall rate and the precipitation phase flag. At this step, the META log protocols are thoroughly inspected and suspicious data is rigorously flagged or discarded. Such events include icing, riming, extreme sea states with wave water influence up to the mast level, true extreme precipitation occurrences, ice-breaking activity to monitor heavy instrument vibration as well as flocks of birds following the ship and instrument cleaning periods. Periods with ODM instrument malfunctions and erroneous data periods (e.g. icing or riming) are marked with value 4 in the flag1 parameter (Table 2). An open issue remains with the influence of the ship’s exhaust plume on air temperature measurements. Such temperature variations can be seen in the data and their occurrence can last from minutes to much longer time periods. A homogenized correction procedure should be ideally provided by the data operators for NAV and MET.
As a next step, the quality-controlled navigated match-up database is applied to the precipitation, air-sea flux and rainfall microphysics modules (Fig. 1) to derive precipitation occurrence and intensity for rain, snow and mixed-phase and the following parameters: turbulent heat fluxes, evaporation, freshwater flux and the PSD gamma distribution parameters, radar reflectivity parameters and convective-stratiform separators for rainfall.
Each minute with precipitation occurrence logged by the ODM contains the reference voltage, the relative wind speed measured by the cup anemometer at the disdrometer and the total number of particles and bins allocated. The data logger automatically produces two particle count PSDs, one for assumed rainfall and one for assumed snowfall, that contain the number of particles in each of the 128 size bins N(bin). The difference between the rain and snow PSDs results from different assumptions on the residence time allowance for liquid and frozen particles. The residence time is additionally logged for rain and snow but not used to derive parameters30. The 128 size bins logarithmically increase in resolution towards smaller particles. The first 12 bins ranging from 0.04 to 0.36 mm in size are not recorded because they are prone to contain artificial signals caused by ship vibration. From N(bin) the PSD density n(bin) in m−3 is calculated by dividing N(bin) by the product of the measurement volume (120 mm length, 22 mm diameter), the integration time (60 s) and the geometrical sum of the relative wind speed and the parameterized terminal fall velocity68. Normalization of n(bin) by the non-constant bin width leads to the number concentration PSD (m−3 mm−1). The 1-min resolution number concentration PSD (nc-PSD) is stored in the OceanRAIN-M files while the raw particle count PSD N(bin) is stored in the OceanRAIN-R files (raw PSD). Both files are temporally discontinuous and contain precipitation minutes only.
The calculation of the rainfall and snowfall rate P in mm h−1 requires the mass (liquid water equivalent) of the particles M(bin) and the terminal fall velocity V(bin) and is calculated after Pruppacher and Klett72 and Großklaus et al.59:
For rainfall with near-spherical drop shapes and constant drop density, the parameterization of V(bin) and M(bin) is chosen after Atlas and Ulbrich73. For the complex problem of snowfall Lempio70 developed a common lump graupel parameterization based on the work of Hogan74, Brandes et al.75 and Macke et al.76, relating the measured cross sectional area to the maximum dimension of the particle. Therein, the product of V(bin) and M(bin) as a function of the cross sectional area remains in the same order of magnitude for a variety of different snow crystals. Because lump graupel is nearly spherical in shape it circumvents the need for a transfer function between the cross sectional area and the maximum dimension of the particles. This parameterization is limited to a size range of frozen hydrometeors from 0.39 to 9 mm diameter. This agrees with the OceanRAIN data: Klepp30 showed that larger snowflake events are rare. In addition, Klepp et al.61 reported that lump graupel was the most common type of precipitation over the cold-season Norwegian Sea.
Edge effects due to partially scanned particles are avoided by only registering particles with their center being within the volume. Coincidence effects of overlapping particles are statistically corrected59,70. The impact of artificial small droplets due to splashing on the disdrometer housing, snow blockage or wet lenses are minimized by the design of the disdrometer device. Additionally, a filtering is implemented that excludes signals exceeding a residence time threshold. A detailed discussion of the ODM data collection and algorithm chain including the parameterizations used for rainfall and snowfall are provided in the OceanRAIN technical reference30.
Each precipitation minute is ingested into the rainfall and snowfall algorithms to derive the theoretical rainfall and theoretical snowfall intensity in mm h−1. The final assignment for each minute of precipitation as rain, snow or mixed-phase precipitation including a precipitation phase probability is calculated using an automated OceanRAIN precipitation phase distinction algorithm77 and a follow-up quality control. This algorithm uses a logistic regression model after Koistinen and Saltikoff78 with the two recommended predictors air temperature and relative humidity and, additionally, the 99th percentile of the PSD particle diameter. The predicted precipitation phase has been compared against a manually determined precipitation phase from 3-hourly SYN of more than four years of OceanRAIN data from RV Polarstern covering all climatic regions and seasons over the Atlantic Ocean. In addition, all three rain, snow and mixed-phase probabilities are simultaneously determined from two individual rain and snow probability distributions independent from each other. Compared to the hitherto time-consuming manual precipitation phase distinction method, an accuracy of 91% is reached for rain and snow, and of 81.2% when including the mixed-phase precipitation phase. This accuracy reduction can be explained by the highly varying rain–snow fraction on the timescale of minutes within the phase transition zone from−3 °C to +6 °C. However, this is a major improvement over the manual precipitation phase distinction method because the SYN data is only updated in 3-hourly intervals during daylight. This new statistical method considerably speeds up the OceanRAIN data post-processing and, additionally, introduces an objective precipitation phase probability for rain, snow and mixed-phase precipitation at 1-min resolution77. This considerably reduces the earlier number of highly uncertain precipitation phase cases requiring visual inspection of atmospheric variables. Moreover, the OceanRAIN precipitation phase probability allows error characterizing precipitation satellite data sets to unveil systematic errors with respect to the precipitation phase.
Because no mixed-phase algorithm with precise rain-snow fraction exists yet, the snowfall intensity is assigned to mixed-phase minutes. Thus, the mixed-phase precipitation intensity is increasingly underestimated with increasing rain (liquid) fraction and inhibits the largest errors compared to the rainfall and snowfall values so that these precipitation intensities should be treated with care. Additionally, the mixed-phase to rain or snow transition may take place over a few meters or hundreds of meters in altitude. Because GPM and CloudSat retrievals discriminate between rain and snow, we decided to introduce mixed-phase precipitation to OceanRAIN so that the dataset can be stratified accordingly. The theoretical rain and snow intensities are kept in the database. This is of importance mainly for the mixed-phase precipitation because the difference between the theoretical rain and snow intensity provides information on the intensity uncertainty and therefore the liquid fraction during that minute. A mixed-phase precipitation algorithm is envisaged for future dataset releases.
As a next step, each minute in the data record is assigned two precipitation flags, called flag1 and flag2. Flag1 assigns the ODM precipitation phase or instruments condition (Table 2). Value 0 assigns a rainfall minute, 1 is snowfall and 2 is mixed-phase precipitation. Because the data in the OceanRAIN-M and OceanRAIN-R files is temporally discontinuous (precipitation-only events) there can be no true-zero value. However, the OceanRAIN-W files are temporally continuous and therefore contain the true–zero (no precipitation) information that is assigned value 3 in flag1. This is especially important for very light precipitation because two sources for 0.00 mm h−1 precipitation rates exist. First, a precipitation minute can either be zero because no precipitation occurred (true-zero; flag1=3) or, second, precipitation particles occurred with zero accumulation (flag1=0, 1, 2). Value 4 assigns an ODM malfunction and 5 represents port times as already mentioned above. This flag is very useful for users aiming to easily stratify the dataset for precipitation phase (see Usage Note section).
Flag2 further stratifies the precipitation events and intensities (Table 3). If the number of assigned bins and number of particles is smaller than three, a single-minute event is considered being an electronic artefact due to vibration issues. Therefore the precipitation rate is set to zero and a true-zero flag is assigned. However, this occurrence can be traced in the number of bins, number of particles and the theoretical rain and snow rate. All flag1=3 values correspond to flag2=10 values (true-zero, no precipitation). The ODM data contains many occurrences of minutes with less than 20 particles and less than 5 bins occupied. The corresponding precipitation rates are mostly insignificant or zero. The reason for these minutes could be either very light precipitation or strong vibration of the instrument resulting in spurious signals. Therefore, these values are assigned flag2=11 and the decision is left to the user whether to consider these insignificant precipitation intensities as real precipitation. Flag2 values between 12 and 17 stratify the precipitation intensity beginning from 0.00 mm h−1 (flag2=12) onward. Value 13 is introduced because the ODM is capable of measuring very light precipitation between 0.01 and 0.09 mm h−1, which is beyond the usual detection threshold of gauges of 0.1 mm h−1. Value 14 contains light precipitation from 0.1 to 0.99 mm h−1 that may be attributed to drizzle-like precipitation regimes, while value 15 contains moderate rates from 1.00 to 9.99 mm h−1 and value 16 covers strong precipitation rates of 10.00 to 49.99 mm h−1. Extreme precipitation cases are contained in class 17 with values from 50 mm h−1 onward. Class 16 and 17 precipitation events are almost certainly convective. Additionally, a physical convective-stratiform precipitation separator is introduced for rainfall (see Rainfall microphysics module section) that supports this assumption. Another way for users to stratify the data is to use thresholds in the number of bins and number of particles allocated.
The output of the precipitation module comprises precipitation occurrence including true-zero minutes, intensity, accumulation, precipitation phase and probability for rain, snow and mixed-phase, 99th percentile of particle diameter, theoretical rain and snow intensities, flag1, flag2, number of particles and number of bins as well as raw PSDs and number concentration PSDs.
Rainfall microphysics module
The rainfall microphysics module enables direct access for experts as well as non-expert users to relevant parameters such as type of rainfall (convective versus stratiform), the main characteristics of the drop size distribution (DSD), and the resulting radar reflectivity at important frequencies for radar rainfall studies. In order to do so, the following state-of-the art approximations and tools are used. Please note that we processed 1-min PSDs for rainfall only due to the higher complexity of deriving such quantities for snow or mixed-phase precipitation. Research is already underway by our team to develop robust modules for snow and mixed-phase precipitation in future releases of the OceanRAIN dataset.
As demonstrated by the pioneering studies of Testud et al.79 and Bringi et al.80, the DSD can be accurately approximated by a normalized gamma distribution:
Normalized gamma distributions of rainfall are controlled by three parameters: No* (also called Nw in the literature), the intercept of the distribution; D0, the median volume diameter of the distribution; and μ, the shape parameter of the distribution. The quality-controlled DSDs are fitted using this normalized gamma formulation. The three resulting parameters are provided in the OceanRAIN files given that the PSD has at least 10 size bins filled with data. Once No* and D0 are retrieved, we classify the PSD as convective or stratiform rain using the physically-based convective-stratiform classification proposed by Thurai et al.81.
Although the normalized distribution framework is now commonly used in rainfall studies, the DSD is still sometimes approximated using the earlier standard gamma distribution (e.g., Tokay and Short82). Therefore, for users wishing to use this formulation to relate to earlier studies of statistical rainfall properties we have also performed standard gamma fits using the same threshold of 10 size bins and provided the intercept parameter N0 of the standard gamma distribution in the OceanRAIN files.
Williams et al.83 highlighted that since the three parameters of the normalized gamma distribution were not statistically independent, this was causing issues in the TRMM and GPM satellite retrievals of rainfall rate. A new framework has therefore been proposed by these authors, based on the two first moments of the mass spectrum: the mass-weighted mean diameter Dm, which is the first moment of the mass spectrum, and σm, the standard deviation of the mass spectrum. These two parameters have been calculated directly from D0 and μ following Williams et al.83; they are included in the OceanRAIN files.
Finally, we have estimated radar reflectivity Z, differential reflectivity ZDR, and specific differential phase KDP at different frequencies (Rayleigh at 3 GHz, C-band at 5.6 GHz, Ku-band at 13.6 GHz and Ka-band at 35 GHz) using the pyTmatrix tool developed by Leinonen84. These parameters help users interested in applications such as satellite radar retrievals of rainfall rate, GPM satellite radar validation, or high-resolution model evaluation. The main assumptions of such T-matrix calculations are the drop shape model and the standard deviation of the canting angle. In order to produce the radar parameters for the OceanRAIN dataset, the drop shape model from Thurai et al.85 and a standard deviation of canting angle of 20° were used, both settings are recommended in pyTmatrix.
Air-Sea Flux module
The precipitation (P) parameter in OceanRAIN is complemented by the turbulent heat fluxes and the evaporation (E) to obtain the freshwater flux (E-P). The evaporation is derived from along-track ship data using a bulk formulation according to Fairall et al.86. This requires the shipboard air temperature, relative humidity, water temperature and absolute wind speed at minute-resolution that are measured at varying heights on different ships (Table 4). From these values the sea surface temperature (SST) and latent as well as sensible heat fluxes are parameterized using the Coupled Ocean-Atmosphere Response Experiment (COARE) bulk flux algorithm86 version 3.0, which is an updated version of Fairall et al.87 including higher latitudes and stronger wind conditions.
The true wind speed measurement u is reduced to the 10 m reference height u10 using a neutral-layer logarithmic wind profile after Tennekes88. The relative humidity serves to determine the specific air humidity qa (g kg−1) and the saturation specific humidity at the sea surface qs (g kg−1) following Murphy and Koop89.
The bulk water temperature Twater measured in sea water inlets between two and seven meters depth (Table 4) is used to calculate the sea surface temperature SST by applying the cool skin parameterization ΔTwater after Donlon et al.90:
The warm-layer effect on the SST is not implemented in OceanRAIN as most ships lack providing a continuous diurnal cycle measurement of the surface radiation budget. Instead, OceanRAIN contains a warm-layer flag (WLF) that indicates the quality of the derived SST product. The warm-layer effect may influence the SST at wind speeds below 6 m s−1 and global radiation exceeding 50 W m−2 (WLF=2). At wind speeds below 2 m s−1 the SST is influenced by convective and molecular heat transports and a strong warm-layer (WLF=1) may be present90,91. In contrast, high wind speeds beyond 6 m s−1 destroy the warm-layer and the effect becomes insignificant (WLF=0).
The COARE algorithm iteratively estimates the stability-dependent scaling parameters for the drag transfer coefficient Cd, the latent heat flux transfer coefficient Ce and the sensible heat flux transfer coefficient Ch. Additionally using the potential temperature θ lead to the bulk flux calculation of the latent heat flux Hl and sensible heat flux Hs in W m−2 after Brunke et al.92:
where is the air density, Cp is the specific heat at constant pressure and Le is the SST-dependent latent heat of vaporization. The additional sensible heat flux caused by the precipitation is implemented after Gosnell et al.93. However, the cooling rate due to snow or mixed-phase precipitation is not implemented in the Gosnell calculation but is expected to have additional influence.
The evaporation in mm h−1 is derived from the turbulent latent heat flux after Fairall et al.87 by:
where is the density of ocean freshwater as a function of temperature. The specific air humidity qa is adjusted if it exceeds the value of qs using qa=qs−0.01 (g kg−1) to avoid unrealistic negative turbulent latent heat fluxes below −25 W m−2 and in turn unrealistic negative evaporation values.
The difference between the evaporation E and the precipitation P yields the ocean surface freshwater flux E-P in mm h−1 into the atmosphere.
After a final quality and consistency check, all measured, observed and derived parameters are written into the temporally continuous 1-min resolution OceanRAIN-W data files (Data Citation 1). All precipitation relevant parameters are stored into the temporally discontinuous 1-min resolution OceanRAIN-M (Data Citation 2) and OceanRAIN-R (Data Citation 3) files that contain precipitation events only and additionally hold the number concentration PSD and the raw particle count PSD. The data is available for each of the eight ships in netCDF and ascii formats (see Data Records section). The OceanRAIN-1.0 dataset provides 696,740 minutes with 414,807 rain, 232,358 snow and 49,575 mixed-phase precipitation including 4,699,282 true-zero minutes (Fig. 1) for all seasons over the global oceans including the high-latitudes. The true-zero precipitation values are important for validating false alarm statistics in satellite products and to analyze the underlying retrieval characteristics. Therefore, OceanRAIN-1.0 tremendously increases the amount of available high quality data.
The OceanRAIN code is available upon request through a scientific cooperation agreement.
OceanRAIN provides users with three dataset versions (OceanRAIN-W, Data Citation 1; OceanRAIN-M, Data Citation 2; OceanRAIN-R, Data Citation 3) for each of the eight ships in the OceanRAIN fleet. The aim is that users can choose the dataset version that best meets their research needs. The along-track point data covers the global oceans from −90° to 90°N latitude and −180° to 180°E longitude from 10 June 2010 to 10 April 2017. The files are named following the convention OceanRAIN-id_RV-ship-name_ship-identifier_UHAM-ICDC_startdate-enddate_v1_0 with .nc or .ascii as file extension. The ship names, ship identifiers as well as start- and end dates are listed in Table 1. The files are produced at Universität Hamburg (file identifier UHAM) and are hosted at the Integrated Climate Data Center (ICDC).
The OceanRAIN-W contains the 1-minute resolution water cycle components of evaporation, precipitation and the freshwater flux along with all meteorological and oceanographic state variables required to derive these fluxes. The dataset is continuous in time and contains 73 parameters and more than 6.83 million minutes of data (Table 5 (available online only)). Typical applications for OceanRAIN-W comprise process studies and statistical analysis as well as satellite validation and re-analysis or model evaluation. OceanRAIN point data can serve as the surface reference and can be collocated with satellite or model data to analyse and improve their error characteristics. Therefore, it is important to highlight that the RVs sampled on the global oceans during all seasons including the cold-season Southern Oceans.
OceanRAIN-M and OceanRAIN-R focus on minutes containing precipitation and are therefore discontinuous in time. Both datasets comprise 37 precipitation-relevant parameters plus the 128 size bin number concentration PSDs (OceanRAIN-M) and raw number count PSDs (OceanRAIN-R) for 696,740 min in total with rain, snow or mixed-phase precipitation (Table 6 (available online only)). The precipitation-related parameters are identical in the three versions of the dataset. Applications for these datasets especially comprise satellite retrieval performance evaluation for liquid and solid precipitation. For this purpose, OceanRAIN-M and OceanRAIN-R supply the user with a convective versus stratiform precipitation classification and contain the main PSD characteristics and the radar reflectivities at important frequencies for radar rainfall studies. This is of special importance for users aiming at TRMM, CloudSat and GPM product and retrieval validation because these satellite missions carry spaceborne radars.
The OceanRAIN version 1.0 datasets are freely available as netCDF and ascii files from the Climate Computing Center (DKRZ) repositories of the World Data Center for Climate (WDCC) and are provided in the Data Citations section. They can also be retrieved through the Integrated Climate Data Center (ICDC) at the University of Hamburg via http://icdc.cen.uni-hamburg.de/1/daten/atmosphere/oceanrain/ and through the OceanRAIN website http://www.oceanrain.org/. A ReadMe file is provided at each of the data repositories to assist the user on ingesting the data.
Precipitation occurrence and accumulation
The OceanRAIN-W absolute precipitation occurrence (including true-zero minutes) across all latitudes and ocean basins is 14.8%. Separated by precipitation phase, rainfall occurs in 8.8%, snowfall in 4.9% and mixed-phase precipitation in 1.1% of the time. A large fraction of this percentage comprises very light precipitation. Without extremely light precipitation minutes with intensities less than 0.01 mm h−1, that are set to zero in the database, the global occurrence reduces to 9.8% (rainfall 6.9%, snowfall 2.6% and mixed-phase precipitation 0.3%). Excluding intensities below 0.1 mm h−1, the equivalent of what a typical rain gauge would be able to measure, the global occurrence reduces to 6.2% (rainfall 4.9%, snowfall 1.2% and mixed phase precipitation 0.1%). This demonstrates that the oceanic precipitation occurrence is mainly driven by very light precipitation. However, the rainfall percentage as a function of precipitation threshold is fairly stable in the tropics and subtropics (e.g. tropics: 4.5% including true-zeros, 3.9% less than 0.01 mm h−1 rainfall occurrences and 3.1% for events larger than 0.1 mm h−1). In contrast, the mid- and high-latitudes are dominated by very light precipitation (e.g. northern high-latitudes: 16.0% including true-zeros, 8.5% less than 0.01 mm h−1 rainfall occurrences and 4.3% for events larger than 0.1 mm h−1).
The global relative precipitation occurrence (all precipitation minutes set to 100%) contains 59.5% rainfall (70.6% less than 0.01 mm h−1 and 79.9% exceeding 0.1 mm h −1). Snowfall occurs in 33.4% (26.5 and 18.8%) of the time and mixed-phase precipitation in 7.1% (2.8 and 1.3%) of the time. The increasing relative rainfall percentages at rates less than 0.01 mm h−1 and those exceeding 0.1 mm h −1 are caused by the strong decrease of the corresponding snowfall and mixed-phase occurrences.
As expected, the very light precipitation has little effect on the precipitation accumulation. In total the OceanRAIN 1.0 dataset contains 8867.6 mm precipitation accumulation. Rainfall dominates with 92.1% while snow contributed 7.4% to this accumulation and mixed-phase as little as 0.5%. These numbers change only marginally for accumulations from intensities above 0.1 mm h−1.
To get an overview of the sampled precipitation over the global oceans, Fig. 3 depicts the relative percentages and absolute accumulations for rain, snow and mixed-phase precipitation as a function of precipitation intensity through the latitudinal belts of both hemispheres. The figure does not account for seasonal variations, e.g. the meridional shift of the intertropical convergence zone. Again, light precipitation dominates the precipitation occurrence (chosen here from 0 to 0.5 mm h−1). Light precipitation occurs least often in the inner tropics with 56.8% of the time. The light rainfall fraction largely increases towards the poles and reaches 88.1% in the northern polar latitudes. The occurrence of light snow and light mixed-phase precipitation below 0.5 mm h−1 ranges between 84.2 and 99.9%. Precipitation between 0.5 and 5 mm h−1 mainly occurs as rainfall and varies between 11.5% in the northern polar latitudes and 36.9% in the northern mid-latitudes. Additionally, the polar and mid-latitudes of both hemispheres contribute between 5.8 and 15.4% of snowfall between 0.5 and 5 mm h−1. Rainfall beyond 5 mm h−1 occurs in 0.4% of the time in the northern polar latitudes and increases towards a maximum of 10.8% in the tropics. However, this occurrence of 10.8% contributes by 76.0% to the total rainfall accumulation in the tropics. These values remain high in the subtropics (61.9 and 74.6%) and the mid-latitudes where this rainfall type contributes between 48.4 and 52.4% to the accumulation. Expectedly, light precipitation has little effect on the accumulation and varies between 2.8 and 6.8%. Towards the polar-latitudes the light rainfall accumulation increases up to 24.3% and reaches 28.4% for snowfall and 87.6% for mixed-phase precipitation.
Variability of water cycle components
An overview on the variability of the water-cycle components in the OceanRAIN-W database across the latitudes is depicted in Fig. 4. The latitudinal precipitation fraction in Fig. 4a shows the occurrence of mixed-phase precipitation poleward of 40°N and 45°S. Snowfall occurs poleward of 44 °N and 50 °S. Solid precipitation is the predominant phase beyond 80 °N and 53 °S. The disparity of the latitudes on both hemispheres is due to the boreal summer sampling of the RVs in the Arctic while the RVs sampled all seasons in the Southern Oceans. The latitudinal precipitation phase as a function of precipitation intensity comprises all 414,807 minutes of rainfall, 232,358 snowfall minutes and 49,575 min with mixed-phase precipitation. The highest rainfall intensities occur in the tropics between −10° and +15° with a record value of 367 mm h−1 onboard RV Meteor on 14 September 2015 in the tropical Atlantic. During 68 minutes of rainfall, the tropical cluster accumulated about 40 mm of rainfall, which mainly occurred during nine consecutive minutes with rainfall exceeding 50 mm h−1.
The joint histogram of the precipitation intensity in Fig. 4b shows that subtropics are characterized by significantly fewer rainfall events while individual events reach intensities of up to 100 mm h−1. The mid-latitude rainfall begins at approx. 35° latitude on both hemispheres. The rainfall intensities in the mid-latitudes are comparable with those measured in the tropics. First, this indicates the importance of mid-latitude cyclones that contribute a major fraction of the global rainfall with high intensities at high wind speeds. Second, this may indicate an undersampling of the intertropical convergence zone rainfall in the OceanRAIN dataset. Ship personnel metadata often reports thunderstorm and torrential rain in the vicinity of the ships while the ship track remains largely dry. Comprehensive sampling of the inner tropical convection cells requires more ships and more years of data collection. The next version of the OceanRAIN dataset will largely improve this sampling. This is also supported by the bin-wise mean for 2° latitude bands. The zonal mean curve shows a strong latitudinal fluctuation and therefore is strongly precipitation event driven. The climatology is only met where the sampling is high, as indicated by the darker colors of the frequency of occurrence in percent. In turn, the zonal mean does not meet the climatology value wherever the sampling is low (e.g. in the inner tropics). The transition from rainfall to snowfall and mixed-phase precipitation occurs at 50°S and 44°N, respectively. However, most of the northern hemisphere cold-season precipitation is measured at latitudes northward of 70°N. This is because the Southern Oceans are sampled in OceanRAIN during all seasons with three ships including the austral winter cruise of RV Polarstern during August 2013. This cruise alone contributed about 40,000 minutes with snowfall at temperatures as low as −30 °C. In contrast, the northern hemisphere is sampled more infrequently by two ships and during the boreal summer only. However, it has to be noted that RV Polarstern crossed the North Pole repeatedly in September. Although there is strong research focus on the Southern Oceans, it is envisaged to improve the northern high-latitude sampling including the cold-season.
Between 0° and 10° north of the equator the evaporation shows a prominent local minimum where the highest rainfall rates occur (Fig. 4c). The evaporation is strongest in the subtropical belts between approx. 20° and 45° on both hemispheres and reaches values of about 1.2 mm h−1. Toward the poles the evaporation rapidly decreases to values below 0.1 mm h−1. The dense sampling of the evaporation leads to a zonal mean curve that approaches the climatology with a local minimum of 0.13 mm h−1 in the inner tropics followed by values in the order of 0.2 mm h−1 in the subtropics. The mid-latitudes show values around 0.05 mm h−1 with a decreasing trend towards the poles.
For the freshwater flux (precipitation minus evaporation), positive values denote a net flux of moisture (evaporation) into the atmosphere, while negative values indicate a net flux into the ocean (precipitation). The high temporal resolution of the data results in a long tail of the distribution towards negative values whenever intense precipitation occurred (Fig. 4d). The negative freshwater flux regimes are located in the inner tropics (−0.15 mm h−1) and the mid-latitudes (−0.2 to −0.4 mm h−1). They are strongly driven by events with intense precipitation. The maxima occur in the subtropics of both hemispheres with values of about 0.1 mm h−1. Again, they meet the climatology because of the dense sampling. Towards the poles the freshwater flux strongly decreases to mean values around zero.
The precipitation microphysics of the OceanRAIN-M database are evaluated in Fig. 5. The log-normal distribution of precipitation exhibits a tremendously high number of small precipitation particles and thus explains that light precipitation occurs much more frequently compared to high precipitation intensities. While the light precipitation intensities contribute most to the precipitation occurrence, moderate to heavy precipitation intensities contribute most to accumulation. This highlights the need to resolve the PSD at both extreme ends which is why a logarithmic size binning is introduced to the ODM470. To demonstrate this, Fig. 5a shows the log(Z) and log(R) values in dBZ and dBR for all 696,740 rainfall events, as well as for subsets of stratiform (139,557 minutes) and convective rainfall (15,823). Fig. 5a resembles Fig. 1 in Dölling et al.94 that consists of 200,548 1-min spectra of cold air advections in Hamburg, Germany. The black part of the distributions holds the number of 1-min spectra with less than 10 bin-sizes occupied in the PSD. The blue part contains the stratiform-classified minutes, with rainfall rates ranging from 0.1 to about 10 mm h−1. The red points show the convective-type rainfall. Convective rain rates are found between 0.5 to 367 mm h−1 in our dataset. The distribution reaches from one-drop spectra at 0.39 mm (−40 dBR, −35 dBZ) to extreme precipitation events at 20 dBR and 55 dBZ. The tail of the distribution reaching 20 dBR and 80 dBZ contains the one-drop spectra at 10 mm diameter (Fig. 5b). From all enclosed points or any subset of the dataset it is possible to derive Z (Rayleigh reflectivity) – R (rainfall rate) relationships to constrain, validate or improve radar-derived and modelled rainfall rate estimates.
Figure 5b depicts the mean number concentration PSDs (bold marks) and their latitudinal variability (small marks) as used in Fig. 3 for all 696,740 precipitation 1-min spectra (414,807 minutes of rainfall, 232,358 snowfall and 49,575 mixed-phase precipitation). The number concentration PSDs resemble those in Klepp30. In contrast to this early version, the OceanRAIN 1.0 dataset comprises a 12-fold increase in the number of 1-min rainfall spectra, a 6-fold increase for snowfall and 2.5-fold for mixed-phase precipitation. The rainfall PSD peaks at a diameter of 0.39 mm with a number concentration of about 1000 m−3 mm−1 and steeply decreases down to about 0.003 m−3 mm−1 for the largest drops of about 6.5 mm diameter. Single drops, however, can reach a diameter of about 9.7 mm and were measured during convective rainfall in the tropics. The snowfall curve peaks at 1100 m−3 mm−1 and descends more gradually towards largest measured snowflakes of 22 mm in diameter. The mixed-phase precipitation curve resembles that of rainfall for diameters between 0.39 and 1 mm diameter. For diameters up to 6 mm it lies between the curves for rainfall and snow. For larger diameters the curve converges towards that of snowfall and reaches the snowfall curve at 14 mm diameter. The largest snowflakes in the mixed-phase distribution reach 19.5 mm in diameter. The number concentration at 0.39 mm is higher for frozen particles than for rain drops because rain drops tend to evaporate faster than frozen particles.
Figure 5c,d summarize the differential reflectivity (Zdr) versus reflectivity (Z) and Zdr versus mass-weighted mean diameter (Dm) relationships for simulated C-Band, Ku-Band and Ka-Band radar frequencies. They resemble the theoretical curves shown in Fig. 7.20 and Fig. 7.23 in Bringi and Chandrasekar95. The ODM470 Zdr is the simulated difference in returned energy between the horizontally and vertically polarized pulses of a radar under the assumption of different radar frequencies. Hence, Zdr is a measure for the oblateness of the rain drops. As expected, increasingly bigger rain drops in Fig. 5d are associated with increasing Zdr, in other words more oblate rain drops. These figures also highlight the large variability of co-variation of polarimetric radar variables with radar frequency.
Precipitation validation using radar, gauge and ODM470 data
In the introduction to this paper we made the claim that the OceanRAIN ODM470 disdrometer was able to mitigate the significant issues commonly encountered using rain gauges or off-the-shelf disdrometers not specifically designed for shipboard operation. During the CAPRICORN experiment from RV Investigator, a micro-rain radar96 (MRR-2) was deployed next to the long-term installed ODM470 and a conventional RM Young Rain Gauge97 type 50202 from 19 March to 14 April 2016 measuring precipitation over the Southern Ocean in the southeastern Australian section. The MRR-2 measures vertical profiles of radar reflectivity at 24 GHz. The improved Doppler spectral processing technique from Maahn and Kollias98 has been used to estimate reflectivity. This setup was therefore the perfect opportunity to substantiate these claims. Fig. 6a shows a joint frequency distribution of reflectivities simulated from the ODM470 PSDs using the pyTmatrix tool and those directly measured by the MRR-2 radar. There is no expectation for such comparisons to be perfect: the first useful radar range bin was about 100 m above the ODM470 disdrometer, the sampling volume of a disdrometer and a radar are very different (although this effect is partly mitigated using 1-minute averages), and the T-matrix simulations of radar reflectivity from disdrometer measurements are not perfect due to assumptions made about the drop shape model and standard deviation of canting angle (errors of 2 dB or more can be expected). Although minimized to the extent possible, through the installation in the mast and the instrument design, nevertheless typical artefacts associated with high winds, drop breakup on the instrument, flow distortion from the ship superstructure, should all produce visible artefacts on such comparisons30. As can be seen from Fig. 6a, the agreement is very good, without any noticeable systematic issue at different reflectivity levels. The correlation between the two measurements is 0.75, which is excellent when considering the previously discussed large differences in sampling volumes. The mean bias in reflectivity is −0.6 dB (ODM470 reflectivity simulations are on average 0.6 dB higher than MRR-2 reflectivities), which is smaller than the expected errors inherent to T-matrix assumptions, and probably smaller than the expected error on the MRR-2 calibration itself. The standard deviation of the difference between the two reflectivities is 3 dB, which again is small when considering all sources of uncertainties affecting these comparisons. Overall, these comparisons clearly showcase the high quality PSD measurements collected by the ODM470 instruments in a wide range of radar reflectivities.
As a second step of the intercomparison, Fig. 6b shows the total accumulation in mm from 1-min precipitation rates of the ODM470 compared to the conventional ship rain gauge over the same time period. Because wind speed belongs to the most influential factors to explain significant undercatch of gauges during rainfall events, the relative wind speed in m s−1 is also depicted. Additionally, 4 bars are added indicating when the relative wind speed was above 15 m s−1, above 20 m s−1, the air temperature was between −3° and +6°C, snowfall occurred or mixed-phase precipitation was present. The cruise track in the Southern Ocean south of Tasmania is depicted in the geographical map inset in Fig. 6b. Rain occurred during 9481 minutes while solid precipitation was relatively rare (773 minutes with mixed-phase and 38 with snow). True-zero minutes are shown in grey. The overall precipitation occurrence is high with 27%. The relative wind speed was for most of the time between 10 m s−1 and 20 m s−1. The accumulation of the ODM470 reached 57.2 mm while the gauge measured 24.9 mm, resulting in a significant gauge undercatch of 56.5%. However, the accumulation curves are in phase for most time steps because of the few solid precipitation occurrences.
To demonstrate that such high undercatch values are not specific for this time period, the third step of the intercomparison in Fig. 6c shows the follow-up RV Investigator cruise out of Wellington from 1 to 31 May 2016 in the Southern Ocean south and southeast of New Zealand, as depicted in the geographical map inset. This cruise is distinguished by its exceptionally high precipitation occurrence of 40% including a large amount of solid precipitation. 6333 out of 44,640 min contained snowfall and 1045 mixed-phase precipitation while rain fell in 10,381 min. During periods of mixed-phase precipitation and snowfall the gauge is expected to detect few to no precipitation. Snowfall during low wind speed conditions will clog the orifice. Once temperatures rise above the freezing level, melting snow produces artificial, delayed meltwater signals. This effect occurs from 4 to 11 May 2016 in Fig. 6c when the accumulation curves for the ODM470 and the gauge are not in phase because of the snowfall and mixed-phase occurrence at wind speeds frequently exceeding 15 m s−1. During this period, the ODM470 accumulated 25.4 mm in total while the gauge measured 5.3 mm, equivalent to 79.3% of undercatch. However, the gauge clearly misses the events seen in the ODM470 accumulation and instead shows a slight, almost linearly increasing signal with time, except for an accumulation phase in the second half of 9 May during which the ODM470 did not record significant precipitation. Instead, during the evening of 8 May, the air temperature was rising from freezing temperatures to 1.4 °C and falls significantly below freezing level again on 10 May. Therefore, melting snow in the gauge orifice explains this gauge accumulation leading to an artificial precipitation event of 2.6 mm. Without this melting event, the undercatch of the gauge would reach 90%.
On 22 May, an intense rain event occurred from 00:40 UTC to 11:31 UTC with a maximum ODM470 precipitation rate of 39.4 mm h−1 resulting in an accumulation of 51.4 mm in the ODM470 data while the gauge accumulated 18.0 mm (54.3% undercatch) at about 12 °C. However, high relative wind speeds exceeding 15 m s−1 and even 20 m s−1 were present during the time period when the ODM470 recorded peak accumulation rates.
During the whole month of May 2016, the gauge missed 48.9% of the precipitation measured by the ODM470 (167.1 mm). The 7.6% better performance of the gauge during May 2016 of 48.9% compared to 56.5% undercatch during March to April 2016 results from the overall lower relative wind speed regime during May. Whereas during May 2016 53% of the rainfall, 73% of the snowfall and 86% of the mixed-phase precipitation fell at wind speeds exceeding 10 m s−1, higher relative wind speeds occurred from March to April 2016 (78, 100, and 98%). Adding to the solid precipitation undercatch, the gauge received significantly less precipitation during rainfall because 26% of the rainfall events occurred at winds exceeding 15 m s−1 in May while this was the case for 40% in March and April 2016.
This observed gauge-undercatch by approximately 50% for rainfall at high relative wind speeds is common in the OceanRAIN database. For snow, the gauge undercatch is in the order of 90% and often reaches 100%. The gauge reliability for rainfall improves for all ships with decreasing wind speeds and reaches good to perfect agreement if the wind decreases significantly below 5 m s−1. However, it has to be considered that in 83% of the time, the relative wind speed in the entire OceanRAIN database of eight ships exceeds the critical threshold of 5 m s−1 when precipitation is present. This value does not vary with precipitation phase (84% rainfall, 80% snowfall, 83% mixed-phase precipitation). Thresholds exceeding 15 m s−1 still reach 27% for rainfall, 17% for snow and 24% for mixed-phase precipitation.
Users requiring surface reference data of precipitation including PSDs should use the OceanRAIN-M (number concentration PSD) or OceanRAIN-R (particle count PSD) files both of which are discontinuous in time. The continuous in time OceanRAIN-W data should be used if the true-zero precipitation information is needed or if additional meteorological and oceanographic parameters are required such as the along-track turbulent heat fluxes, the evaporation or the freshwater flux.
Usage of precipitation flags
Two precipitation flags are consistently used in all three versions of the dataset in order to discriminate the precipitation phase and intensity. Flag1 (Table 2) assigns a value for rainfall (0), snowfall (1), mixed-phase precipitation (2) or no precipitation (true zero) (3) to each minute. Note that the data records in the OceanRAIN-M and OceanRAIN-R files are discontinuous in time and thus contain precipitation minutes only. However, it is important to note, that very light precipitation may result in a precipitation rate of 0.00 mm h−1 (flag1=0, 1, 2) due to an insignificant number of particles measured. In contrast to these events, during true-zero precipitation occurrences in the OceanRAIN-W files (flag1=3), the ODM470 relative wind speed and reference voltage is set to −888.88 in order to distinguish these minutes from the missing value (−999.99). Flag1 also indicates instrument outages with a value of 4 and harbour times with a value of 5.
Precipitation flag2 allows to further classify precipitation intensities (Table 3). True-zero minutes (flag1=3) correspond to flag2=10. Value 11 assigns spurious precipitation with less than 20 particles with less than 5 bins occupied. The associated insignificant precipitation rates may result from very light precipitation or strong vibration of the instrument resulting in artificial signals. The decision is left to the user whether to consider or discard these values. Value 12 contains all precipitation minutes with a rate of 0.00 mm h−1. Values of 13 to 17 indicate increasing precipitation rates according to Table 3.
Additionally, we provide a physically-derived convective-stratiform precipitation index for rainfall. Modules for snow and mixed-phase precipitation are under development for future releases of the OceanRAIN dataset. Nevertheless, Rayleigh reflectivities are provided for snow and mixed phase but should be treated with care. Due to the unknown liquid-to-solid ratio, mixed-phase precipitation carries the largest uncertainty and should therefore be treated with care, as well. Precipitation minutes that fail the quality check are either set to true-zero precipitation or instrument malfunction. Rejection reasons include interference by wave water, riming, icing, birds and maintenance. However, the original values can be tracked in the OceanRAIN-W dataset using the number of particles, number of bins and the theoretical rain and snowfall values.
Point-to-area representativeness of precipitation
The validation of satellite-derived precipitation estimates using surface-based point measurements of precipitation includes a number of challenges to be tackled. Loew et al.65 provide a comprehensive overview of these difficulties and ways how to address them properly. Resolution differences in space and time belong to the most important issues, constituting the long-standing point-to-area (p2a) problem. This problem influences both, the detection as well as the estimation of precipitation rates from point measurements within the satellite pixel area. However, the resulting representativeness error can be minimized by increasing the representativeness of point measurements such as OceanRAIN compared to an area as a satellite sensor sees it. To achieve this for OceanRAIN, Burdanowitz et al.66 conducted a synthetic study using weather radar data from the Rain In Cumulus clouds over the Ocean (RICO) campaign on Barbuda99. From the radar data, synthetic ship tracks within a synthetic area of a typical passive microwave satellite pixel were randomly matched to each other. This idealized study allows to isolate the representativeness error and to derive statistical adjustments for the along-track averaged precipitation rates with respect to the area-averaged precipitation rates to minimize the representativeness error. Both adjustments use parameters calculated from OceanRAIN, like the along-track precipitation event duration and the median-normalized along-track precipitation rate after the previous adjustment. The derived adjustment procedures from the purely synthetic radar study have been applied to OceanRAIN precipitation data to improve the evaluation of precipitation estimates from the HOAPS23,28 satellite climatology (Hamburg Ocean Atmosphere Parameters and fluxes from Satellite). The result has been compared in Burdanowitz67 using the Cloud Physical Properties (CPP) product100. A direct comparison in combination with a case study shows first, that the statistical adjustments perform well for most cases. Only widespread precipitation of uniform intensity needs no adjustment due to its spatially uniform precipitation distribution. Second, the p2a problem contributes more strongly to differences between HOAPS and OceanRAIN than the precipitation regime classified using precipitation area and intensity from CPP. The statistical adjustment of OceanRAIN is particularly crucial for rather coarsely resolved satellite estimates of 0.5° and above and marks an important step towards a more representative precipitation validation of satellite data over the ocean.
How to cite this article: Klepp, C. et al. OceanRAIN, a new in-situ shipboard global ocean surface-reference dataset of all water cycle components. Sci. Data 5:180122 doi: 10.1038/sdata.2018.122 (2018).
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Trenberth, K. E., Dai, A., Rasmussen, R. M. & Parsons, D. B. The Changing Character of Precipitation. Bull. Amer. Meteor. Soc. 84 (9): 1205–1217 (2003).
Adler, R. F., Gu, G. & Huffman, G. J. Estimating climatological bias errors for the global precipitation climatology project (GPCP). J. Appl. Meteor. Clim 51 (1): 84–99 (2012).
Intergovernmental Panel on Climate Change (IPCC). Climate Change 2013: The Physical Science Basis, WG1 119-158 (Cambridge Univ. Press, 2014).
Global Climate Observing System (GCOS). Status of the Global Observing System for Climate. Full Report No. 195 http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf (2015).
Trenberth, K. E., Fasullo, J. T. & Kiehl, J. Earth’s Global Energy Budget. Bull. Amer. Meteor. Soc. 90 (3): 311–323 (2009).
Romanova, V. et al. Sea surface freshwater flux estimates from GECCO, HOAPS and NCEP. Tellus A 62, 435–452, https://doi.org/10.1111/j.1600-0870.2010.00447.x (2010).
Wijesekera, H. W. et al. Upper ocean heat and freshwater budgets in the eastern Pacific warm pool. J. Geophys. Res 110, 1–27, https://doi.org/10.1029/2004JC002511 (2005).
Weller, R. A. et al. Sensors for physical fluxes at the sea surface: energy, heat, water, salt. Ocean Science 4, 247–263 (2008).
Levizzani, V., Laviola, S. & Cattani, E. Detection and measurement of snowfall from Space. Rem. Sens 3, 145–166 (2011).
Bojinski, S. et al. The Concept of Essential Climate Variables in Support of Climate Research, Applications, and Policy. Bull. Amer. Meteor. Soc. 95 (9): 1431–1443 (2014).
Curry, J. A. et al. SEAFLUX. Bull. Amer. Meteor. Soc 85, 409–424, https://doi.org/10.1175/BAMS-85-3-409 (2004).
Kidd, C. & Huffman, G. J. Global precipitation measurement. Meteorol. Appl 18, 334–353, https://doi.org/10.1002/met.284 (2011).
Huffman, G. J. & Klepp, C. Meeting Summary: Fifth Workshop of the International Precipitation Working Group. Bull. Amer. Meteor. Soc https://doi.org/10.1175/BAMS-D-11-00030.1 (2011).
Kummerow, C., Barnes, W., Kozu, T., Shiue, J. & Simpson, J. The Tropical Rainfall Measuring Mission (TRMM) Sensor Package. J. Atmos. Oceanic. Technol 15, 809–817, https://doi.org/10.1175/1520-0426(1998)015<0809:TTRMMT>2.0.CO;2 (1998).
Ellis, T. D., L’Ecuyer, T. S., Haynes, J. M. & Stephens, G. L. How often does it rain over the global oceans? The perspective from CloudSat. Geophys. Res. Lett. 36, L03815 (2009).
Mitrescu, C., L’Ecuyer, T., Haynes, J., Miller, S. & Turk, J. CloudSat precipitation profiling algorithm – model description. J. Appl. Meteor. Climatol 49, 991–1003, https://doi.org/10.1175/2009JAMC2181.1 (2010).
Hou, A. Y. et al. The Global Precipitation Measurement Mission. Bull. Amer. Meteor. Soc 95, 701–722, https://doi.org/10.1175/BAMS-D-13-00164.1 (2014).
Joyce, R. J., Janowiak, J. E., Arkin, P. A. & Xie, P. Cmorph: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydrometeor 5 (3): 487–503 (2004).
Huffman, G. J. et al. The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales. J. Hydrometeor 8 (1): 38–55 (2007).
Huffman, G. J. GPM merged radiometer global .1 x.1 deg half-hourly precipitation retrieval – NASA Goddard Earth Sciences Data and Information Services Center (2015).
Okamoto, K., Ushio, T., Iguchi, T., Takahashi, N. & Iwanami, K. The global satellite mapping of precipitation (GSMaP) project. IEEE 5, 3414–3416 (2005).
Hsu, K. l., Gao, X., Sorooshian, S. & Gupta, H. V. Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks. J. Appl. Meteor 36 (9): 1176–1190 (1997).
Andersson, A. et al. The Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data – HOAPS-3. Earth Sys. Sci. Data 2 (2): 215–234 (2010).
Stephens, G. L. et al. The CLOUDSAT Mission and The A-TRAIN: A New Dimension of Space-Based Observations of Clouds and Precipitation. Bull. Amer. Meteor. Soc. 83 (12): 1771–1790 (2002).
Huffman, G. J., Bolvin, D. T. & Nelkin, E. J. Integrated multisatellite retrievals for GPM (IMERG). Precipitation Measurement Missions Technical documentationhttps://pmm.nasa.gov/sites/default/files/document_files/IMERG_doc.pdf (2015).
Petty, G. W. An inter-comparison of oceanic precipitation frequencies from 10 special sensor microwave/imager rain rate algorithms and shipboard present weather reports. J. Geophys. Res. 102, 1757–1777 (1997).
Béranger, K., Barnier, B., Gulev, S. & Crepon, M. Comparing 20 years of precipitation estimates from different sources over the world ocean. Ocean Dyn. 56, 104–138 (2006).
Andersson, A. et al. Evaluation of HOAPS-3 ocean surface freshwater flux components. J. Appl. Meteor. Clim 50, 379–398, https://doi.org/10.1175/2010JAMC2341.1 (2011).
Stephens, G.L. et al. An update on Earth’s energy balance in light of the latest global observations. Nature Geoscience 5, 691–696, https://doi.org/10.1038/ngeo1580 (2012).
Klepp, C. The Oceanic Shipboard Precipitation Measurement Network for Surface Validation – OceanRAIN. Atmos. Res., Special issue of the International Precipitation Working Group (IPWG) 163, 74–90, https://doi.org/10.1016/j.atmosres.2014.12.014 (2015).
Smith, S. et al. The Data Management System for the Shipboard Automated Meteorological and Oceanographic System (SAMOS) Initiative in Proceedings of OceanObs'09: Sustained Ocean Observations and Information for Society Vol. 29 (ed., Hall, J. 959–968 (ESA Publication WPP-306, 2010).
Bumke, K., Fennig, K., Strehz, A., Mecking, R. & Schröder, M. HOAPS precipitation validation with ship borne rain gauge measurements over the Baltic Sea. Tellus A 64, 18486, https://doi.org/10.3402/tellusa.v64i0.18486 (2012).
Strangeways, I. Improving precipitation measurement. Int. J. Clim 24 (11): 1443–1460 (2004).
Hayes, S., Mangum, L., Picaut, J., Sumi, A. & Takeuchi, K. TOGA-TAO: A moored array for real-time measurements in the tropical Pacific Ocean. J. Geophys. Res. 72, 339–347 (1991).
Bourlès, B. et al. The Pirata Program: History, accomplishments, and future directions. Bull. Amer. Meteor. Soc 89, 1111–1125, https://doi.org/10.1175/2008BAMS2462.1 (2008).
Serra, Y. Precipitation Measurements from the Tropical Moored Array: A Review and Look Ahead Quart. J. Royal Met. Soc. https://doi.org/10.1002/qj.3287 (special issue of the International Precipitation Working Group, 2018).
da Silva, A. M., Young, C. C. & Levitus, S. Atlas of Surface Marine Data 1994, Volume 1: Algorithms and Procedures (NOAA Atlas NESDIS 6, U.S. Department of Commerce, NOAA, NESDIS (1994).
Josey, S. A., Kent, E. C. & Taylor, P. K. The Southampton Oceanography Centre (SOC) Ocean-Atmosphere Heat. Momentum and Freshwater Flux Atlas, Report No. 6 (Southampton Oceanography Centre, 1998).
Woodruff, S. D. et al. ICOADS Release 2.5: extensions and enhancements to the surface marine meteorological archive. Int. J. Clim 31, 951–967, https://doi.org/10.1002/joc.2103 (2011).
Berry, D. I. & Kent, E. C. Air - sea fluxes from ICOADS: the construction of a new gridded dataset with uncertainty estimates. Int. J. Clim 31, 987–1001, https://doi.org/10.1002/joc.2059 (2011).
Send, U. et al. OceanSITES in Proceedings of OceanObs'09: Sustained Ocean Observations and Information for Society Vol. 2 (ed., Hall, J. ) 913–922 (ESA Publication WPP-306, 2010).
Weller, R. A., Bradley, E. F. & Lukas, R. The Interface or Air–Sea Flux Component of the TOGA Coupled Ocean–Atmosphere Response Experiment and Its Impact on Subsequent Air–Sea Interaction Studies. J. Atmos. Oceanic. Technol 21, 223–257, 787 https://doi.org/10.1175/1520-0426%282004%29021%3C0223%3ATIOAFC%3E2.0.CO%3B2 (2004).
Goodison, B. E. Accuracy of Canadian snow gauge measurements. J. Appl. Meteor 17 (10): 1542–1548 (1978).
Yuter, S. E. & Parker, W. S. Rainfall measurement on ship revisited: The 1997 PACS TEPPS cruise. J. Appl. Meteor 40 (6): 1003–1018 (2001).
Michelson, D. B. Systematic correction of precipitation gauge observations using analyzed meteorological variables. J. Hydrol. 290 (3-4): 161–177 (2004).
Miriovsky, B. J. et al. An experimental study of small-scale variability of radar reflectivity using disdrometer observations. J. Appl. Meteor 43 (1): 106–118 (2004).
Lee, G. W. & Zawadzki, I. Variability of Drop Size Distributions: Time-Scale Dependence of the Variability and Its Effects on Rain Estimation. J. Appl. Meteor 44 (2): 241–255 (2005).
Rosenfeld, D. & Ulbrich, C. W. Cloud microphysical properties, processes, and rainfall estimation opportunities. Radar and Atmospheric Science: A Collection of Essays in Honor of David Atlas, Meteor. Monogr. Amer. Meteor. Soc 52, 237–258 (2003).
Bringi, V. N. & Chandrasekar, V. Polarimetric Doppler Weather Radar: Principles and Applications (Cambridge University Press, 2001).
Tokay, A., Kruger, A. & Krajewski, W. Comparison of drop size distribution measurements by impact and optical disdrometers. J. Appl. Meteor 40, 2083–2097 (2001).
Taylor, P. K. Intercomparison and validation of ocean–atmosphere energy flux fields. Final report of the Joint WCRP/SCOR Working Group on Air–Sea Fluxes (WCRP-112, WMO/TD-1036, 2000).
Michaelides, S. Precipitation: Advances in Measurement, Estimation and Prediction (Springer, ISBN: 978-3-540-77654-3, 2008).
Intergovernmental Panel on Climate Change (IPCC). Climate Change 2013: The Physical Science Basis, WG1 255–316 (Cambridge Univ. Press, 2014).
Maggioni, V., Meyers, P. C. & Robinson, M. D. A Review of Merged High-Resolution Satellite Precipitation Product Accuracy during the Tropical Rainfall Measuring Mission (TRMM) Era. J. Hydrometeor 17 (4): 1101–1117 (2016).
Fairall, C. et al. Observations to Quantify Air Sea Fluxes and their Role in Climate Variability and Predictability in Proceedings of OceanObs'09: Sustained Ocean Observations and Information for Society Vol. 2 (ed., Hall, J. ) 299–313 (ESA Publication WPP-306, 2010).
Stephens, G.L. et al. Dreary state of precipitation in global models. J. Geophys. Res 115, D24211, https://doi.org/10.1029/2010JD014532 (2010).
Nguyen, H., Protat, A., Kumar, V., Rikus, L. & Whimpey, M. A regional forecast model evaluation of statistical rainfall properties using CPOL radar observations in different precipitation regimes over Darwin, Australia. Quart. J. Roy. Meteor. Soc 141, 2337–2349, https://doi.org/10.1002/qj.2525 (2015).
Nguyen, H., Protat, A., Rikus, L., Zhu, H. & Whimpey, M. Sensitivity of the ACCESS forecast model statistical rainfall properties to resolution. Quart. J. Roy. Meteor. Soc 143, 1967–1977, https://doi.org/10.1002/qj.3056 (2017).
Großklaus, M., Uhlig, K. & Hasse, L. An optical disdrometer for use in high wind speeds. J. Atmos. Ocean. Technol 15, 1051–1059 (1998).
Marshall, J. et al. The Labrador Sea deep convection experiment. Bull. Amer. Meteor. Soc. 79, 2033–2058 (1998).
Klepp, C., Bumke, K., Bakan, S. & Bauer, P. Ground validation of oceanic snowfall detection in satellite climatologies during LOFZY. Tellus A 62, 469–480, https://doi.org/10.1111/j.1600-0870.2010.00459.x (2010).
Brümmer, B. et al. Characteristics and impact of a gale-force storm field over the Norwegian Sea. Tellus A 62, 481–496, https://doi.org/10.1111/j.1600-0870.2010.00448.x (2010).
Bumke, K. & Seltmann, J. Analysis of measured drop size spectra over land and sea. ISRN Meteorology (2012); 1–10 (2012).
Bumke, K., König-Langlo, G., Kinzel, J. & Schröder, M. HOAPS and ERA-interim precipitation over the sea: validation against shipboard in situ measurements. Atm. Meas. Techn 9 (5): 2409–2423 (2016).
Loew, A. et al. Validation practices for satellite-based Earth observation data across communities. Rev. Geophys 55, 779–817, https://doi.org/10.1002/2017RG000562 (2017).
Burdanowitz, J., Klepp, C., Bakan, S. & Buehler, S. A. Simulation of Ship-Track versus Satellite-Sensor Differences in Oceanic Precipitation Using an Island-Based Radar. Rem. Sens 9, 593, https://doi.org/10.3390/rs9060593 (2017).
Burdanowitz, J., Klepp, C., Bakan, S. & Buehler, S. A. Towards an along-track validation of HOAPS precipitation using OceanRAIN optical disdrometer data over the Atlantic Ocean. Quart. J. Royal Meteorol. Soc https://doi.org/10.1002/qj.3248 (2018).
Clemens, M. Machbarkeitsstudie zur räumlichen Niederschlagsanalyse aus Schiffsmessungen über der Ostsee (PhD thesis, Christian-Albrechts-Universität: Kiel, 2002).
Bumke, K. et al. Accurate areal precipitation measurements over the land and sea (APOLAS). Newsletter BALTEX 6, 2004).
Lempio, G., Bumke, K. & Macke, A. Measurement of solid precipitation with an optical disdrometer. Adv. Geosci 10, 91–97 (2007).
Lanza, L. G. & Vuerich, E. The WMO Field Intercomparison of Rain Intensity Gauges. Atmos. Res. 94 (4): 534–543 (2009).
Pruppacher, H. R. & Klett, J. D. Microphysics of Clouds and Precipitation (Springer, ISBN 978-0792342113, 1978).
Atlas, D. & Ulbrich, C. The Physical Basis for Attenuation-Rainfall Relationships and the Measurement of Rainfall Parameters by Combined Attenuation and Radar Methods. J. Rech. Atmos 8, 275–298 (1974).
Hogan, A. Objective estimates of airborne snow properties. J. Atmos. Ocean. Technol. 11, 432–444 (1994).
Brandes, E. A., Ikeda, K., Zhang, G., Schönhuber, M. & Rasmussen, R. M. A statistical and physical description of hydrometeor distributions in Colorado snowstorms using a video disdrometer. J. Appl. Met. Clim 46 (5): 634–650 (2007).
Macke, A., Francis, P. N., Mc Farquhar, G. M. & Kinne, S. The role of ice particle shapes and size distributions in the single scattering properties of cirrus clouds. J. Atmos. Sci. 55 (17): 2874–2883 (1998).
Burdanowitz, J., Klepp, C. & Bakan, S. An automatic precipitation-phase distinction algorithm for optical disdrometer data over the global ocean. Atmos. Meas. Tech 9, 1637–1652, https://doi.org/10.5194/amt-9-1637-2016 (2016).
Koistinen, J. & Saltikoff, E. Experience of customer products of accumulated snow, sleet and rain. COST75 Advanced Weather Radar Systems 397–406 (1998).
Testud, J., Oury, S., Black, R. A., Amayenc, P. & Dou, X. K. The concept of ‘‘normalized’’ distribution to describe raindrop spectra: A tool for cloud physics and cloud remote sensing. J. Appl. Meteor 40, 1118–1140, https://doi.org/10.1175/1520-0450(2001)040,1118:TCONDT.2.0.CO;2 (2001).
Bringi, V. N., Huang, G.-J., Chandrasekar, V. & Gorgucci, E. A methodology for estimating the parameters of a gamma raindrop size distribution model from polarimetric radar data: Application to a squall-line event from the TRMM/Brazil campaign. J. Atmos. Oceanic Technol 19, 633–645, https://doi.org/10.1175/1520-0426(2002)019,0633:AMFETP.2.0.CO;2 (2002).
Thurai, M., Bringi, V. N. & May, P. T. CPOL radar-derived drop size distribution statistics of stratiform and convective rain for two regimes in Darwin, Australia. J. Atmos. Oceanic Technol 27, 932–942, https://doi.org/10.1175/2010JTECHA1349.1 (2010).
Tokay, A. & Short, D. A. Evidence from tropical raindrop spectra of the origin of rain from stratiform and convective clouds. J. Appl. Meteor 35, 355–371 (1996).
Williams, C. R. et al. Describing the shape of raindrop size distributions using uncorrelated raindrop mass spectrum parameters. J. Appl. Meteor. Climatol 53, 1282–1296, https://doi.org/10.1175/JAMC-D-13-076.1 (2014).
Leinonen, J. High-level interface to T-matrix scattering calculations: architecture, capabilities and limitations. Optics Express 22, 1655, https://doi.org/10.1364/OE.22.001655 (2014).
Thurai, M., Huang, G. J., Bringi, V. N., Randeu, W. L. & Schönhuber, M. Drop Shapes, Model Comparisons, and Calculations of Polarimetric Radar Parameters in Rain. J. Atmos Ocean. Tech 24, 1019–1032, https://doi.org/10.1175/JTECH2051.1 (2007).
Fairall, C. W., Bradley, E. F., Hare, J. E., Grachev, A. & Edson, J. Bulk Parameterization of Air-Sea Fluxes: Updates and Verification for the COARE Algorithm. Amer. Meteo. Soc 571–591 (2003).
Fairall, C. W., Bradley, E. F., Edson, J., Rogers, D. & Young, G. Bulk Parameterization of Air-Sea fluxes for Tropical Ocean-Global Atmosphere Coupled-Ocean Atmosphere Response Experiment. J. Geophys. Res. 101 (C2): 3747–3764 (1996).
Tennekes, H. The Logarithmic Wind Profile. J. Atmos. Sci. 30, 234–238 (1972).
Murphy, D. M. & Koop, T. Review of the vapour pressures of ice and supercooled water for atmospheric applications. Quart. J. Roy. Met. Soc 131 (608): 1539–1565 (2005).
Donlon, C. J. et al. Toward Improved Validation of Satellite Sea Surface Skin Temperature Measurements for Climate Research. Amer. Met. Soc 353–369 (2002).
Fairall, C. W. et al. Cool-skin and warm-layer effects on sea surface temperature. J. Geophys. Res. 101 (C1): 1295–1308 (1996).
Brunke, M. A., Fairall, C. W., Zeng, X., Eymard, L. & Curry, J. A. Which Bulk Aerodynamic Algorithm are Least Problematic in Computing Ocean Surface Turbulent Fluxes? Amer. Met. Soc 619–635 (2003).
Gosnell, R., Fairall, C. W. & Webster, P. J. The sensible heat of rainfall in the tropical ocean. J. Geophys. Res. 100 (C9): 18437 (1995).
Dölling, I. G., Joss, J. & Riedl, J. Systematic variations of Z–R-relationships from drop size distributions measured in northern Germany during seven years. Atmos. Res. 47-48, 635–649 (1998).
Bringi, V. & Chandrasekar, V. Polarimetric Doppler Weather Radar: Principles and Applications https://doi.org/10.1017/CBO9780511541094 (Cambridge University Press, 2001).
Klugmann, D., Heinsohn, K. & Kirtzel, H. A low cost 24 GHz FM-CW Doppler radar rain profiler. Contr. Atmos. Phys 69, 247–253 (1996).
Young, R. M. The Model 50202 Precipitation Gauge. R.M. Young Technical Description http://www.youngusa.com/products/3/17.html (2018).
Maahn, M. & Kollias, P. Improved Micro Rain Radar snow measurements using Doppler spectra post-processing. Atmos. Meas. Tech. Discuss 5, 4771–4808, https://doi.org/10.5194/amtd-5-4771-2012 (2012).
Rauber, R.M. et al. Rain in Shallow Cumulus Over the Ocean: The RICO Campaign. Bull. Am. Meteorol. Soc 88, 1912–1928, https://doi.org/10.1175/BAMS-88-12-1912 (2007).
Roebeling, R. A. & Holleman, I. SEVIRI rainfall retrieval and validation using weather radar observations. J. Geophys. Res 114, D21202, https://doi.org/10.1029/2009JD012102 (2009).
Klepp, C. et al. World Data Center for Climate (WDCC) at DKRZ https://doi.org/10.1594/WDCC/OceanRAIN-W (2017)
Klepp, C. et al. World Data Center for Climate (WDCC) at DKRZ https://doi.org/10.1594/WDCC/OceanRAIN-M (2017)
Klepp, C. et al. World Data Center for Climate (WDCC) at DKRZ https://doi.org/10.1594/WDCC/OceanRAIN-R (2017)
The sustained funding by Initiative Pro Klima (Mabanaft, Mabanaft Deutschland, Petronord and OIL! Tankstellen GmbH & Co. KG) enabled the development and operation as well as the research on this dataset. We greatly appreciate this support and gratefully thank Tanja Thiele, Gerhard Grambow, Volker Tiedemann, Ulrich Freudental and Jan Falke. The project is additionally funded by CliSAP/CEN, University of Hamburg, and the Max Planck Society (MPG). We are grateful to the ship operators, onboard weather station personnel and research institutes for their continuous support of the instrument deployment, data delivery, maintenance and remote communication: German Research Fleet Coordination Center at the University of Hamburg, Alfred-Wegener Institute, Marum Bremen, Brise Research, German Weather Service, Bundesamt für Seeschifffahrt und Hydrographie (BSH), CSIRO, Marine National Facility and University of Washington. We are grateful for the support of Australian Antarctic Science project 4292, to Tom Warner for financial support and ongoing instrument and data management, and to ROW Management, Ltd. for permission to deploy the equipment aboard MS The World. The authors would like to thank Olaf Dahl, Carsten Benecke and Jörn Matthies for their disdrometer hardware and software support. We further thank Remon Sadikni, Hans-Hermann Winter and Heinke Höcke for the data publication support. We acknowledge Kyla Drushka and Carol-Ann Clayson for delivering raw data into the project. The essential contributions of all unnamed colleagues and ship personnel are explicitly acknowledged.
The authors declare no competing interests.
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Klepp, C., Michel, S., Protat, A. et al. OceanRAIN, a new in-situ shipboard global ocean surface-reference dataset of all water cycle components. Sci Data 5, 180122 (2018). https://doi.org/10.1038/sdata.2018.122