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Adam Eshel
,
Hagit Messer
,
Harald Kunstmann
,
Pinhas Alpert
, and
Christian Chwala

Abstract

Using signal level measurements from commercial microwave links (CMLs) has proven to be a valuable tool for near-ground 2D rain mapping. Such mapping is commonly based on spatial interpolation methods, where each CML is considered as a point measurement instrument located at its center. The validity of the resulted maps is tested against radar observations. However, since radar has limitations, accuracy of CML-based reconstructed rain maps remains unclear. Here we provide a quantitative comparison of the performance of CML-based spatial interpolation methods for rain mapping by conducting a systematic analysis: first by quantifying the performance of maps generated from semisynthetic CML data, and thereafter turning to real-data analysis of the same rain events. A radar product of the German Weather Service serves as ground truth for generating semisynthetic data, in which several temporal aggregations of the radar rainfall fields are used to create different decorrelation distances. The study was done over an area of 225 × 245 km2 in southern Germany, with 808 CMLs. We compare the performance of two spatial interpolation methods—inverse distance weighting and ordinary kriging—in two cases: where each CML is represented as a single point, and where three points are used. The points’ measurements values in the latter are determined using an iterative algorithm. The analysis of both cases is based on a 48-h rain event. The results reconfirm the validity of CML-based rain retrieval, showing a slight systematic performance improvement when an iterative algorithm is applied so each CML is represented by more than a single point, independent of the interpolation method.

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Martin Raspaud
,
David Hoese
,
Adam Dybbroe
,
Panu Lahtinen
,
Abhay Devasthale
,
Mikhail Itkin
,
Ulrich Hamann
,
Lars Ørum Rasmussen
,
Esben Stigård Nielsen
,
Thomas Leppelt
,
Alexander Maul
,
Christian Kliche
, and
Hrobjartur Thorsteinsson

Abstract

PyTroll (http://pytroll.org) is a suite of open-source easy-to-use Python packages to facilitate processing and efficient sharing of Earth Observation (EO) satellite data. The PyTroll software is intended for both 24/7 real-time operations as well as research and development. PyTroll grew out of the need to provide a resilient and agile platform that can respond quickly to new user needs and new data sources. PyTroll, being open source, stimulates international collaboration, which is vital with the rapid increase of satellite information availability. The PyTroll software development is strongly user driven and has grown over the past eight years from a collaborative effort between the Danish and Swedish national meteorological services to encompass a worldwide community with active contributors. PyTroll is being used at least operationally in the national meteorological services of Denmark, Norway, Sweden, Finland, Germany, Switzerland, Italy, Estonia, and Latvia. However, given its simplicity, minimal demand on user resources, and community-driven approach, it also encourages and facilitates usage of EO data for individual applications. While PyTroll was originally developed to cater to the needs of the atmospheric remote sensing community, it could be equally useful for land and ocean applications and within hydrology. This article provides an overview of PyTroll, with examples showing the capability of some of the core packages.

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Jim Thomson
,
Joe Talbert
,
Alex de Klerk
,
Adam Brown
,
Mike Schwendeman
,
Jarett Goldsmith
,
Julie Thomas
,
Corey Olfe
,
Grant Cameron
, and
Christian Meinig

Abstract

The effects of biofouling on a wave measurement buoy are examined using concurrent data collected with two Datawell Waveriders at Ocean Station P: one heavily biofouled at the end of a 26-month deployment, the other newly deployed and clean. The effects are limited to the high-frequency response of the buoy and are correctly diagnosed with the spectral “check factors” that compare horizontal and vertical displacements. A simple prediction for the progressive change in frequency response during biofouling reproduces the check factors over time. The bulk statistical parameters of significant wave height, peak period, average period, and peak direction are only slightly affected by the biofouling because the contaminated frequencies have very low energy throughout the comparison dataset.

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Christopher Velden
,
Bruce Harper
,
Frank Wells
,
John L. Beven II
,
Ray Zehr
,
Timothy Olander
,
Max Mayfield
,
Charles “CHIP” Guard
,
Mark Lander
,
Roger Edson
,
Lixion Avila
,
Andrew Burton
,
Mike Turk
,
Akihiro Kikuchi
,
Adam Christian
,
Philippe Caroff
, and
Paul McCrone

The history of meteorology has taught us that weather analysis and prediction usually advances by a series of small, progressive studies. Occasionally, however, a special body of work can accelerate this process. When that work pertains to high-impact weather events that can affect large populations, it is especially notable. In this paper we review the contributions by Vernon F. Dvorak, whose innovations using satellite observations of cloud patterns fundamentally enhanced the ability to monitor tropical cyclones on a global scale. We discuss how his original technique has progressed, and the ways in which new spaceborne instruments are being employed to complement Dvorak's original visions.

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Christopher Velden
,
Bruce Harper
,
Frank Wells
,
John L. Beven II
,
Ray Zehr
,
Timothy Olander
,
Max Mayfield
,
Charles“Chip” Guard
,
Mark Lander
,
Roger Edson
,
Lixion Avila
,
Andrew Burton
,
Mike Turk
,
Akihiro Kikuchi
,
Adam Christian
,
Philippe Caroff
, and
Paul McCrone
Full access
Christian Stolle
,
Mariana Ribas-Ribas
,
Thomas H. Badewien
,
Jonathan Barnes
,
Lucy J. Carpenter
,
Rosie Chance
,
Lars Riis Damgaard
,
Ana María Durán Quesada
,
Anja Engel
,
Sanja Frka
,
Luisa Galgani
,
Blaženka Gašparović
,
Michaela Gerriets
,
Nur Ili Hamizah Mustaffa
,
Hartmut Herrmann
,
Liisa Kallajoki
,
Ryan Pereira
,
Franziska Radach
,
Niels Peter Revsbech
,
Philippa Rickard
,
Adam Saint
,
Matthew Salter
,
Maren Striebel
,
Nadja Triesch
,
Guenther Uher
,
Robert C. Upstill-Goddard
,
Manuela van Pinxteren
,
Birthe Zäncker
,
Paul Zieger
, and
Oliver Wurl
Full access
Christian Stolle
,
Mariana Ribas-Ribas
,
Thomas H. Badewien
,
Jonathan Barnes
,
Lucy J. Carpenter
,
Rosie Chance
,
Lars Riis Damgaard
,
Ana María Durán Quesada
,
Anja Engel
,
Sanja Frka
,
Luisa Galgani
,
Blaženka Gašparović
,
Michaela Gerriets
,
Nur Ili Hamizah Mustaffa
,
Hartmut Herrmann
,
Liisa Kallajoki
,
Ryan Pereira
,
Franziska Radach
,
Niels Peter Revsbech
,
Philippa Rickard
,
Adam Saint
,
Matthew Salter
,
Maren Striebel
,
Nadja Triesch
,
Guenther Uher
,
Robert C. Upstill-Goddard
,
Manuela van Pinxteren
,
Birthe Zäncker
,
Paul Zieger
, and
Oliver Wurl

Abstract

The sea surface microlayer (SML) at the air–sea interface is <1 mm thick, but it is physically, chemically, and biologically distinct from the underlying water and the atmosphere above. Wind-driven turbulence and solar radiation are important drivers of SML physical and biogeochemical properties. Given that the SML is involved in all air–sea exchanges of mass and energy, its response to solar radiation, especially in relation to how it regulates the air–sea exchange of climate-relevant gases and aerosols, is surprisingly poorly characterized. MILAN (Sea Surface Microlayer at Night) was an international, multidisciplinary campaign designed to specifically address this issue. In spring 2017, we deployed diverse sampling platforms (research vessels, radio-controlled catamaran, free-drifting buoy) to study full diel cycles in the coastal North Sea SML and in underlying water, and installed a land-based aerosol sampler. We also carried out concurrent ex situ experiments using several microsensors, a laboratory gas exchange tank, a solar simulator, and a sea spray simulation chamber. In this paper we outline the diversity of approaches employed and some initial results obtained during MILAN. Our observations of diel SML variability show, for example, an influence of (i) changing solar radiation on the quantity and quality of organic material and (ii) diel changes in wind intensity primarily forcing air–sea CO2 exchange. Thus, MILAN underlines the value and the need of multidiciplinary campaigns for integrating SML complexity into the context of air–sea interaction.

Free access
Bart Geerts
,
Scott E. Giangrande
,
Greg M. McFarquhar
,
Lulin Xue
,
Steven J. Abel
,
Jennifer M. Comstock
,
Susanne Crewell
,
Paul J. DeMott
,
Kerstin Ebell
,
Paul Field
,
Thomas C. J. Hill
,
Alexis Hunzinger
,
Michael P. Jensen
,
Karen L. Johnson
,
Timothy W. Juliano
,
Pavlos Kollias
,
Branko Kosovic
,
Christian Lackner
,
Ed Luke
,
Christof Lüpkes
,
Alyssa A. Matthews
,
Roel Neggers
,
Mikhail Ovchinnikov
,
Heath Powers
,
Matthew D. Shupe
,
Thomas Spengler
,
Benjamin E. Swanson
,
Michael Tjernström
,
Adam K. Theisen
,
Nathan A. Wales
,
Yonggang Wang
,
Manfred Wendisch
, and
Peng Wu

Abstract

One of the most intense air mass transformations on Earth happens when cold air flows from frozen surfaces to much warmer open water in cold-air outbreaks (CAOs), a process captured beautifully in satellite imagery. Despite the ubiquity of the CAO cloud regime over high-latitude oceans, we have a rather poor understanding of its properties, its role in energy and water cycles, and its treatment in weather and climate models. The Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) was conducted to better understand this regime and its representation in models. COMBLE aimed to examine the relations between surface fluxes, boundary layer structure, aerosol, cloud, and precipitation properties, and mesoscale circulations in marine CAOs. Processes affecting these properties largely fall in a range of scales where boundary layer processes, convection, and precipitation are tightly coupled, which makes accurate representation of the CAO cloud regime in numerical weather prediction and global climate models most challenging. COMBLE deployed an Atmospheric Radiation Measurement Mobile Facility at a coastal site in northern Scandinavia (69°N), with additional instruments on Bear Island (75°N), from December 2019 to May 2020. CAO conditions were experienced 19% (21%) of the time at the main site (on Bear Island). A comprehensive suite of continuous in situ and remote sensing observations of atmospheric conditions, clouds, precipitation, and aerosol were collected. Because of the clouds’ well-defined origin, their shallow depth, and the broad range of observed temperature and aerosol concentrations, the COMBLE dataset provides a powerful modeling testbed for improving the representation of mixed-phase cloud processes in large-eddy simulations and large-scale models.

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