The development of algorithms for the retrieval of water cycle components from satellite data – such as total column water vapor content (TCWV), precipitation (P), latent heat flux, and evaporation (E) – has seen much progress in the past 3 decades. In the present study, we compare six recent satellite-based retrieval algorithms and ERA5 (the European Centre for Medium-Range Weather Forecasts' fifth reanalysis) freshwater flux (E−P) data regarding global and regional, seasonal and interannual variation to assess the degree of correspondence among them. The compared data sets are recent, freely available, and documented climate data records (CDRs), developed with a focus on stability and homogeneity of the time series, as opposed to instantaneous accuracy. One main finding of our study is the agreement of global ocean means of all E−P data sets within the uncertainty ranges of satellite-based data. Regionally, however, significant differences are found among the satellite data and with ERA5. Regression analyses of regional monthly means of E, P, and E−P against the statistical median of the satellite data ensemble (SEM) show that, despite substantial differences in global E patterns, deviations among E−P data are dominated by differences in P throughout the globe. E−P differences among data sets are spatially inhomogeneous. We observe that for ERA5 long-term global E−P is very close to 0 mm d−1 and that there is good agreement between land and ocean mean E−P, vertically integrated moisture flux divergence (VIMD), and global TCWV tendency. The fact that E and P are balanced globally provides an opportunity to investigate the consistency between E and P data sets. Over ocean, P (nearly) balances with E if the net transport of water vapor from ocean to land (approximated by over-ocean VIMD, i.e., ∇⋅(vq)ocean) is taken into account. On a monthly timescale, linear regression of Eocean-∇⋅(vq)ocean with Pocean yields R2=0.86 for ERA5, but smaller R2 values are found for satellite data sets. Global yearly climatological totals of water cycle components (E, P, E−P, and net transport from ocean to land and vice versa) calculated from the data sets used in this study are in agreement with previous studies, with ERA5 E and P occupying the upper part of the range. Over ocean, both the spread among satellite-based E and the difference between two satellite-based P data sets are greater than E−P, and these remain the largest sources of uncertainty within the observed global water budget. We conclude that, for a better understanding of the global water budget, the quality of E and P data sets needs to be improved, and the uncertainties more rigorously quantified.

Our study forms the oceanic counterpart to numerous observational studies over land concerning the sensitivity of extreme precipitation to a change in air temperature. We explore the sensitivity of oceanic precipitation to changing sea surface temperature (SST) by exploiting two novel datasets at high resolution. First, we use the Ocean Rainfall And Ice-phase precipitation measurement Network (OceanRAIN) as an observational along-track shipboard dataset at 1 min resolution. Second, we exploit the most recent European Reanalysis version 5 (ERA5) at hourly resolution on a 31 km grid. Matched with each other, ERA5 vertical velocity allows the constraint of the OceanRAIN precipitation. Despite the inhomogeneous sampling along ship tracks, OceanRAIN agrees with ERA5 on the average latitudinal distribution of precipitation with fairly good seasonal sampling. However, the 99th percentile of OceanRAIN precipitation follows a super Clausius–Clapeyron scaling with a SST that exceeds 8.5 % K−1 while ERA5 precipitation scales with 4.5 % K−1. The sensitivity decreases towards lower precipitation percentiles, while OceanRAIN keeps an almost constant offset to ERA5 due to higher spatial resolution and temporal sampling. Unlike over land, we find no evidence for a decreasing precipitation event duration with increasing SST. ERA5 precipitation reaches a local minimum at about 26 ∘C that vanishes when constraining vertical velocity to strongly rising motion and excluding areas of weak correlation between precipitation and vertical velocity. This indicates that instead of moisture limitations as over land, circulation dynamics rather limit precipitation formation over the ocean. For the strongest rising motion, precipitation scaling converges to a constant value at all precipitation percentiles. Overall, high resolutions in observations and climate models are key to understanding and predicting the sensitivity of oceanic precipitation extremes to a change in SST.

A configuration of the High-Altitude Long-Range Research Aircraft (HALO) as a remote sensing cloud observatory is described, and its use is illustrated with results from the first and second Next-Generation Aircraft Remote Sensing for Validation (NARVAL) field studies. Measurements from the second NARVAL (NARVAL2) are used to highlight the ability of HALO, when configured in this fashion, to characterize not only the distribution of water condensate in the atmosphere, but also its impact on radiant energy transfer and the covarying large-scale meteorological conditions—including the large-scale velocity field and its vertical component. The NARVAL campaigns with HALO demonstrate the potential of airborne cloud observatories to address long-standing riddles in studies of the coupling between clouds and circulation and are helping to motivate a new generation of field studies.

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.

The Hamburg Ocean Atmosphere Parameters and fluxes from Satellite data (HOAPS) passive‐microwave precipitation scan product is compared with the Ocean Rainfall And Ice‐phase precipitation measurement Network (OceanRAIN) surface‐based precipitation reference dataset over the global ocean. For the first time, we apply statistical point‐to‐area adjustments and along‐track averaging to ship‐based OceanRAIN precipitation data over the Atlantic Ocean to represent collocated precipitation rates better within a HOAPS satellite pixel. The statistical adjustments reduce the HOAPS–OceanRAIN root‐mean‐square error strongly from 2.65 to 1.01 mm/hr. Overall, the point‐to‐area effect impacts HOAPS–OceanRAIN differences more strongly than the precipitation regime. Higher‐resolution satellite data indicate that these adjustments work best for the most convective‐like precipitation cases, while some rather stratiform‐like precipitation cases would need no adjustment. Excluding precipitation rates below the HOAPS sensitivity threshold of 0.3 mm/hr reduces the difference in average precipitation rates between HOAPS hits and false detections combined and OceanRAIN hits and misses combined to 2%. This precipitation‐rate difference lies below the uncertainty obtained from resampling of about 10%. Without false detections, the HOAPS precipitation rate of hits‐only exceeds that of OceanRAIN by 50%. Most of the HOAPS false detections follow from cases when precipitation occurs within the HOAPS pixel but off the ship track. Consequently, these apparent false detections lead to an overestimation of HOAPS precipitation rates compared with OceanRAIN, particularly in the inner Tropics and partly the midlatitudes, where clustered convective precipitation occurs most frequently. Misses cause underestimated HOAPS precipitation rates, mainly in mid and high latitudes. However, for a HOAPS validation, apparent false detections need to be considered in addition to hits of OceanRAIN precipitation rates, to which we successfully applied statistical point‐to‐area adjustments.

The point-to-area problem strongly complicates the validation of satellite-based precipitation estimates, using surface-based point measurements. We simulate the limited spatial representation of light-to-moderate oceanic precipitation rates along ship tracks with respect to areal passive microwave satellite estimates using data from a subtropical island-based radar. The radar data serves to estimate the discrepancy between point-like and areal precipitation measurements. From the spatial discrepancy, two statistical adjustments are derived so that along-track precipitation ship data better represent areal precipitation estimates from satellite sensors. The first statistical adjustment uses the average duration of a precipitation event as seen along a ship track, and the second adjustment uses the median-normalized along-track precipitation rate. Both statistical adjustments combined reduce the root mean squared error by 0.24 mm h − 1 (55%) compared to the unadjusted average track of 60 radar pixels in length corresponding to a typical ship speed of 24–34 km h − 1 depending on track orientation. Beyond along-track averaging, the statistical adjustments represent an important step towards a more accurate validation of precipitation derived from passive microwave satellite sensors using point-like along-track surface precipitation reference data.

This discussion paper introduces a new automatic precipitation phase distinction algorithm for optical disdrometer data over the global ocean. The introduction highlights current short-comings of in-situ precipitation measurements, which eventually serve as reference to microwave precipitation records. The authors present the benefits of ODMs related to extreme meteorological conditions and poor data density and highlight the need of timesaving post-processing procedures. In this respect, convincing arguments regarding automatic precipitation phase (PP) distinction algorithms of different complexity are brought up, as these not only accelerate data analysis, but minimize subjectivity inherent to manual observations. After a brief instrument description and a mathematical background, the manual reference data set as well as the OceanRAIN data basis is introduced. It becomes clear that the high fraction of snow and mixed-phase precipitation within OceanRAIN under extreme meteorological conditions, next to high measurement frequencies, is beneficial to creating the PP distinction algorithms. The following three subsections focus on the different PP distinction algorithms, including elaborations on their (dis-)advantages. The main findings are well illustrated in form of several demonstrative figures. A short discussion towards the end relates the findings to recent publications. Comparing statistical scores derived in this work with those of former studies suggests that the new phase

The lack of high-quality in situ surface precipitation data over the global ocean so far limits the capability to validate satellite precipitation retrievals. The first systematic ship-based surface precipitation data set OceanRAIN (Ocean Rainfall And Ice-phase precipitation measurement Network) aims at providing a comprehensive statistical basis of in situ precipitation reference data from optical disdrometers at 1 min resolution deployed on various research vessels (RVs). Deriving the precipitation rate for rain and snow requires a priori knowledge of the precipitation phase (PP). Therefore, we present an automatic PP distinction algorithm using available data based on more than 4 years of atmospheric measurements onboard RV Polarstern that covers all climatic regions of the Atlantic Ocean. A time-consuming manual PP distinction within the OceanRAIN post-processing serves as reference, mainly based on 3-hourly present weather information from a human observer. For automation, we find that the combination of air temperature, relative humidity, and 99th percentile of the particle diameter predicts best the PP with respect to the manually determined PP. Excluding mixed phase, this variable combination reaches an accuracy of 91 % when compared to the manually determined PP for 149 635 min of precipitation from RV Polarstern. Including mixed phase (165 632 min), an accuracy of 81.2 % is reached for two independent PP distributions with a slight snow overprediction bias of 0.93. Using two independent PP distributions represents a new method that outperforms the conventional method of using only one PP distribution to statistically derive the PP. The new statistical automatic PP distinction method considerably speeds up the data post-processing within OceanRAIN while introducing an objective PP probability for each PP at 1 min resolution.

Introduction Conclusions References

Today, latent heat flux and precipitation over the global ocean surface can be determined from microwave satellite data as a basis for estimating the related fields of the ocean surface freshwater flux. The Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data (HOAPS) is the only generally available satellite-based dataset with consistently derived global fields of both evaporation and precipitation and hence of freshwater flux for the period 1987-2005. This paper presents a comparison of the evaporation E, precipitation P, and the resulting freshwater flux E - P in HOAPS with recently available reference datasets from reanalysis and other satellite observation projects as well as in situ ship measurements. In addition, the humidity and wind speed input parameters for the evaporation are examined to identify sources for differences between the datasets. Results show that the general climatological patterns are reproduced by all datasets. Global mean time series often agree within about 10% of the individual products, while locally larger deviations may be found for all parameters. HOAPS often agrees better with the other satellite-derived datasets than with the in situ or the reanalysis data. The agreement usually improves in regions of good in situ sampling statistics. The biggest deviations of the evaporation parameter result from differences in the near-surface humidity estimates. The precipitation datasets exhibit large differences in highly variable regimes with the largest absolute differences in the ITCZ and the largest relative biases in the extratropical storm-track regions. The resulting freshwater flux estimates exhibit distinct differences in terms of global averages as well as regional biases. In comparison with long-term mean global river runoff data, the ocean surface freshwater balance is not closed by any of the compared fields. The datasets exhibit a positive bias in E - P of 0.2 - 0.5 mm day(-1), which is on the order of 10% of the evaporation and precipitation estimates.

We characterize the climatological features of the double inter-tropical convergence zones (DITCZs) over the western Indian Ocean during November-December by a synergistic analysis of the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite (HOAPS III) data (1988-2005) and the National Aeronautics and Space Administration's (NASA's) A-Train data (2002-2009). We investigate rainfall, freshwater flux and cloud liquid water, cloud fraction and relative humidity over the DITCZs. In addition, the daily rainfall data from the Global Precipitation Climatology Project (GPCP) are used to document the DITCZs during the El Nino southern oscillation (ENSO) events. An analysis of the GPCP data shows that the DITCZs are clearly discernible during strong ENSO events (1997, 2002 and 2006), in sharp contrast to the DITCZs in the eastern Pacific Ocean, where they are absent during ENSOs. Further, these convergence zones on either side of the equator are of short duration, approximately 3-6 pentads during November and December. All satellite sensor data sets consistently capture the major features of DITCZs. As an accurate simulation of DITCZs in coupled global climate models remains a challenge, the results from the present study would provide a platform for evaluating these models.

State-of-the-art satellite derived and reanalysis based precipitation climatologies show remarkably large differences in detection, amount, variability and temporal behavior of precipitation over the oceans. The uncertainties are largest for light precipitation within the ITCZ and for cold season high-latitude precipitation including snowfall. Our HOAPS (Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite data, www.hoaps.org) precipitation retrieval exhibits fairly high accuracy in such regions compared to our ground validation data. However, the statistical basis for a conclusive validation has to be significantly improved with comprehensive ground validation efforts. However, existing in-situ instruments are not designed for precipitation measurements under high wind speeds on moving ships.