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George J. Huffman and Christian Klepp

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Christian-Philipp Klepp, Stephan Bakan, and Hartmut Graßl

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Case studies of rainfall, derived from Special Sensor Microwave Imager (SSM/I) satellite data during the passage of individual cyclones over the North Atlantic, are presented to enhance the knowledge of rainfall processes associated with frontal systems. A multisatellite method is applied for complete coverage of the North Atlantic twice a day. Different SSM/I precipitation algorithms have been tested for individual cyclones and compared to the Global Precipitation Climatology Project (GPCP) datasets. An independent rainfall pattern and intensity validation method is presented using voluntary observing ship (VOS) datasets and Advanced Very High Resolution Radiometer (AVHRR) images.

Intense cyclones occur frequently in the wintertime period, with cold fronts propagating far south over the North Atlantic. Following upstream, large cloud clusters are frequently embedded in the cellular structured cold air of the backside regions, which produce heavy convective rainfall events, especially in the region off Newfoundland around 50°N. These storms can be easily identified on AVHRR images. It transpired that only the SSM/I rainfall algorithm of Bauer and Schlüssel is sensitive enough to detect the rainfall patterns and intensities observed by VOS for those cyclone types over the North Atlantic. In contrast, the GPCP products do not recognize this backside rainfall, whereas the frontal rainfall conditions are well represented in all tested datasets. This is suggested from the results of an intensive intercomparison study with ship reports from the time period of the Fronts and Atlantic Storm Track Experiment (FASTEX) field campaign. For this purpose, a new technique has been developed to transfer ship report codes into rain-rate estimates. From the analysis of a complete life cycle of a cyclone, it follows that these mesoscale backside rainfall events contribute up to 25% to the total amount of rainfall in North Atlantic cyclones.

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Jörg Burdanowitz, Louise Nuijens, Bjorn Stevens, and Christian Klepp

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Three state-of-the-art satellite climatologies are analyzed for their ability to observe light rain from predominantly shallow, warm clouds over the subtropical North Atlantic Ocean trade winds (1998–2005). HOAPS composite (HOAPS-C), version 3.2; TMPA, version 7; and GPCP 1 Degree Daily (1DD), version 1.2, are compared with ground-based S-Pol radar data from the Rain in Cumulus over the Ocean (RICO; winter 2004/05) campaign and Micro Rain Radar data from the Barbados Cloud Observatory (2010–12). Winter rainfall amounts to one-third of annual rainfall, whereby light rain from warm clouds dominates. Daily rain occurrence and rain intensity during RICO largely differ among the satellite climatologies. TMPA best captures the frequent light rain events, only missing 7% of days on which the S-Pol radar detects rain, whereas HOAPS-C misses 33% and GPCP 1DD misses 56%. Algorithm constraints mainly cause these differences. In HOAPS-C also few available passive microwave (PMW) sensor overpasses limit its performance. TMPA outperforms HOAPS-C when only comparing nonmissing time steps, yet HOAPS-C can detect rain for S-Pol rain-covered areas down to 2%. In GPCP 1DD’s algorithm, the underestimated rain occurrence derived from PMW scanners is linked to the overestimated rain intensity, being constrained by the GPCP monthly satellite–gauge combination, whereby IR sensors determine the timing. Algorithm improvements in version 1.2 increased the rain occurrence by 50% relative to version 1.1. In version 7 of TMPA, algorithm corrections in PMW sounder data largely improved the rain detection relative to version 6. TMPA best represents light rain in the North Atlantic trades, followed by HOAPS-C and GPCP 1DD.

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Richard M. Schulte, Christian D. Kummerow, Christian Klepp, and Gerald G. Mace

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A significant part of the uncertainty in satellite-based precipitation products stems from differing assumptions about drop size distributions (DSDs). Satellite radar-based retrieval algorithms rely on DSD assumptions that may be overly simplistic, while radiometers further struggle to distinguish cloud water from rain. We utilize the OceanRAIN-1.0 dataset to examine the impact of DSD variability on the ability of satellite measurements to accurately estimate warm rainfall rates. We use the binned disdrometer counts and a simple model of the atmosphere to simulate observations for three satellite architectures. Two are similar to existing instrument combinations on the GPM Core Observatory and CloudSat, while the third is a theoretical triple frequency radar/radiometer architecture. Using an optimal estimation framework, we find that the assumed DSD shape can have a large impact on retrieved rain rate. A 3-parameter normalized gamma DSD model is sufficient for describing and retrieving the DSDs observed in the OceanRAIN dataset. Assuming simpler single moment DSD models can lead to significant biases in retrieved rain rate, on the order of 100%. Differing DSD assumptions could thus plausibly explain a large portion of the disagreement in satellite-based precipitation estimates.

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Axel Andersson, Christian Klepp, Karsten Fennig, Stephan Bakan, Hartmut Grassl, and Jörg Schulz

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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 EP 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 EP of 0.2–0.5 mm day−1, which is on the order of 10% of the evaporation and precipitation estimates.

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Bjorn Stevens, Felix Ament, Sandrine Bony, Susanne Crewell, Florian Ewald, Silke Gross, Akio Hansen, Lutz Hirsch, Marek Jacob, Tobias Kölling, Heike Konow, Bernhard Mayer, Manfred Wendisch, Martin Wirth, Kevin Wolf, Stephan Bakan, Matthias Bauer-Pfundstein, Matthias Brueck, Julien Delanoë, André Ehrlich, David Farrell, Marvin Forde, Felix Gödde, Hans Grob, Martin Hagen, Evelyn Jäkel, Friedhelm Jansen, Christian Klepp, Marcus Klingebiel, Mario Mech, Gerhard Peters, Markus Rapp, Allison A. Wing, and Tobias Zinner

Abstract

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.

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