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P. R. A. Brown
,
A. J. Illingworth
,
A. J. Heymsfield
,
G. M. McFarquhar
,
K. A. Browning
, and
M. Gosset

Abstract

The purpose of this paper is to assess the potential of a spaceborne 94-GHz radar for providing useful measurements of the vertical distribution and water content of ice clouds on a global scale.

Calculations of longwave (LW) fluxes for a number of model ice clouds are performed. These are used to determine the minimum cloud optical depth that will cause changes in the outgoing longwave radiation or flux divergence within a cloud layer greatear than 10 W m−2, and in surface downward LW flux greater than 5 W m−2, compared to the clear-sky value. These optical depth values are used as the definition of a “radiatively significant” cloud. Different “thresholds of radiative significance” are calculated for each of the three radiation parameters and also for tropical and midlatitude cirrus clouds. Extensive observational datasets of ice crystal size spectra from midlatitude and tropical cirrus are then used to assess the capability of a radar to meet these measurement requirements. A radar with a threshold of −30 dBZ should detect 99% (92%) of “radiatively significant” clouds in the midlatitudes (Tropics). This detection efficiency may be reduced significantly for tropical clouds at very low temperatures (−80°C).

The LW flux calculations are also used to establish the required accuracy within which the optical depth should be known in order to estimate LW fluxes or flux divergence to within specified limits of accuracy. Accuracy requirements are also expressed in terms of ice water content (IWC) because of the need to validate cloud parameterization schemes in general circulation models (GCMs). Estimates of IWC derived using radar alone and also using additional information to define the mean crystal size are considered. With crystal size information available, the IWC for samples with a horizontal scale of 1–2 km may be obtained with a bias of less than 8%. For IWC larger than 0.01 g m−3, the random error is in the range +50% to −35%, whereas for a value of 0.001 g m−3 the random error increases to between +80% and −45%. This level of accuracy also represents the best that may be achieved for estimates of the cloud optical depth and meets the requirements derived from LW flux calculations. In the absence of independent particle size information, the random error is within the range +85% to −55% for IWC greater than 0.01 g m−3. For the same IWC range, the estimated bias is few than ±15%. This accuracy is sufficient to provide useful constraints on GCM cloud parameteriation schemes.

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S. J. Rennie
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S. L. Dance
,
A. J. Illingworth
,
S. P. Ballard
, and
D. Simonin

Abstract

The assimilation of Doppler radar radial winds for high-resolution NWP may improve short-term forecasts of convective weather. Using insects as the radar target, it is possible to provide wind observations during convective development. This study aims to explore the potential of these new observations, with three case studies. Radial winds from insects detected by four operational weather radars were assimilated using three-dimensional variational data assimilation (3D-Var) into a 1.5-km resolution version of the Met Office Unified Model, using a southern U.K. domain and no convective parameterization. The effect on the analyzed wind was small, with changes in direction and speed up to 45° and 2 m s−1, respectively. The forecast precipitation was perturbed in space and time but not substantially modified. Radial wind observations from insects show the potential to provide small corrections to the location and timing of showers, but not to completely relocate convergence lines. Overall, quantitative analysis indicated the observation impact in the three case studies was small and neutral. However, the small sample size and possible ground clutter contamination issues preclude unequivocal impact estimation. The study shows the potential positive impact of insect winds; future operational systems using dual-polarization radars that are better able to discriminate between insects and clutter returns should provide a much greater impact on forecasts.

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M. Haeffelin
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S. Crewell
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A. J. Illingworth
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G. Pappalardo
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H. Russchenberg
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M. Chiriaco
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K. Ebell
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R. J. Hogan
, and
F. Madonna
Full access
A. J. Illingworth
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D. Cimini
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C. Gaffard
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M. Haeffelin
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V. Lehmann
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U. Löhnert
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E. J. O’Connor
, and
D. Ruffieux

Abstract

A new generation of high-resolution (1 km) forecast models promises to revolutionize the prediction of hazardous weather such as windstorms, flash floods, and poor air quality. To realize this promise, a dense observing network, focusing on the lower few kilometers of the atmosphere, is required to verify these new forecast models with the ultimate goal of assimilating the data. At present there are insufficient systematic observations of the vertical profiles of water vapor, temperature, wind, and aerosols; a major constraint is the absence of funding to install new networks. A recent research program financed by the European Union, tasked with addressing this lack of observations, demonstrated that the assimilation of observations from an existing wind profiler network reduces forecast errors, provided that the individual instruments are strategically located and properly maintained. Additionally, it identified three further existing European networks of instruments that are currently underexploited, but with minimal expense they could deliver quality-controlled data to national weather services in near–real time, so the data could be assimilated into forecast models. Specifically, 1) several hundred automatic lidars and ceilometers can provide backscatter profiles associated with aerosol and cloud properties and structures with 30-m vertical resolution every minute; 2) more than 20 Doppler lidars, a fairly new technology, can measure vertical and horizontal winds in the lower atmosphere with a vertical resolution of 30 m every 5 min; and 3) about 30 microwave profilers can estimate profiles of temperature and humidity in the lower few kilometers every 10 min. Examples of potential benefits from these instruments are presented.

Full access
A. J. Illingworth
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D. Cimini
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A. Haefele
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M. Haeffelin
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M. Hervo
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S. Kotthaus
,
U. Löhnert
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P. Martinet
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I. Mattis
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E. J. O’Connor
, and
R. Potthast

Abstract

To realize the promise of improved predictions of hazardous weather such as flash floods, wind storms, fog, and poor air quality from high-resolution mesoscale models, the forecast models must be initialized with an accurate representation of the current state of the atmosphere, but the lowest few kilometers are hardly accessible by satellite, especially in dynamically active conditions. We report on recent European developments in the exploitation of existing ground-based profiling instruments so that they are networked and able to send data in real time to forecast centers. The three classes of instruments are i) automatic lidars and ceilometers providing backscatter profiles of clouds, aerosols, dust, fog, and volcanic ash, the last two being especially important for air traffic control; ii) Doppler wind lidars deriving profiles of wind, turbulence, wind shear, wind gusts, and low-level jets; and iii) microwave radiometers estimating profiles of temperature and humidity in nearly all weather conditions. The project includes collaboration from 22 European countries and 15 European national weather services, which involves the implementation of common operating procedures, instrument calibrations, data formats, and retrieval algorithms. Currently, data from 265 ceilometers in 19 countries are being distributed in near–real time to national weather forecast centers; this should soon rise to many hundreds. One wind lidar is currently delivering real time data rising to 5 by the end of 2019, and the plan is to incorporate radiometers in 2020. Initial data assimilation tests indicate a positive impact of the new data.

Open access

Cloudnet

Continuous Evaluation of Cloud Profiles in Seven Operational Models Using Ground-Based Observations

A. J. Illingworth
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R. J. Hogan
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E.J. O'Connor
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D. Bouniol
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M. E. Brooks
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J. Delanoé
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D. P. Donovan
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J. D. Eastment
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N. Gaussiat
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J. W. F. Goddard
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M. Haeffelin
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H. Klein Baltink
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O. A. Krasnov
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J. Pelon
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J.-M. Piriou
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A. Protat
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H. W. J. Russchenberg
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A. Seifert
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A. M. Tompkins
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G.-J. van Zadelhoff
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F. Vinit
,
U. Willén
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D. R. Wilson
, and
C. L. Wrench

The Cloudnet project aims to provide a systematic evaluation of clouds in forecast and climate models by comparing the model output with continuous ground-based observations of the vertical profiles of cloud properties. In the models, the properties of clouds are simplified and expressed in terms of the fraction of the model grid box, which is filled with cloud, together with the liquid and ice water content of the clouds. These models must get the clouds right if they are to correctly represent both their radiative properties and their key role in the production of precipitation, but there are few observations of the vertical profiles of the cloud properties that show whether or not they are successful. Cloud profiles derived from cloud radars, ceilometers, and dual-frequency microwave radiometers operated at three sites in France, Netherlands, and the United Kingdom for several years have been compared with the clouds in seven European models. The advantage of this continuous appraisal is that the feedback on how new versions of models are performing is provided in quasi-real time, as opposed to the much longer time scale needed for in-depth analysis of complex field studies. Here, two occasions are identified when the introduction of new versions of the ECMWF and Météo-France models leads to an immediate improvement in the representation of the clouds and also provides statistics on the performance of the seven models. The Cloudnet analysis scheme is currently being expanded to include sites outside Europe and further operational forecasting and climate models.

Full access
A. J. Illingworth
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A. Battaglia
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J. Bradford
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M. Forsythe
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P. Joe
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P. Kollias
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K. Lean
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M. Lori
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J.-F. Mahfouf
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S. Melo
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R Midthassel
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Y. Munro
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J. Nicol
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R. Potthast
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M. Rennie
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T. H. M. Stein
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S. Tanelli
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F. Tridon
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C. J. Walden
, and
M. Wolde

Abstract

This paper presents a conically scanning spaceborne Dopplerized 94-GHz radar Earth science mission concept: Wind Velocity Radar Nephoscope (WIVERN). WIVERN aims to provide global measurements of in-cloud winds using the Doppler-shifted radar returns from hydrometeors. The conically scanning radar could provide wind data with daily revisits poleward of 50°, 50-km horizontal resolution, and approximately 1-km vertical resolution. The measured winds, when assimilated into weather forecasts and provided they are representative of the larger-scale mean flow, should lead to further improvements in the accuracy and effectiveness of forecasts of severe weather and better focusing of activities to limit damage and loss of life. It should also be possible to characterize the more variable winds associated with local convection. Polarization diversity would be used to enable high wind speeds to be unambiguously observed; analysis indicates that artifacts associated with polarization diversity are rare and can be identified. Winds should be measurable down to 1 km above the ocean surface and 2 km over land. The potential impact of the WIVERN winds on reducing forecast errors is estimated by comparison with the known positive impact of cloud motion and aircraft winds. The main thrust of WIVERN is observing in-cloud winds, but WIVERN should also provide global estimates of ice water content, cloud cover, and vertical distribution, continuing the data series started by CloudSat with the conical scan giving increased coverage. As with CloudSat, estimates of rainfall and snowfall rates should be possible. These nonwind products may also have a positive impact when assimilated into weather forecasts.

Open access
A. J. Illingworth
,
H. W. Barker
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A. Beljaars
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M. Ceccaldi
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H. Chepfer
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N. Clerbaux
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J. Cole
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J. Delanoë
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C. Domenech
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D. P. Donovan
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S. Fukuda
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M. Hirakata
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R. J. Hogan
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A. Huenerbein
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P. Kollias
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T. Kubota
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T. Nakajima
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T. Y. Nakajima
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T. Nishizawa
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Y. Ohno
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H. Okamoto
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R. Oki
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K. Sato
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M. Satoh
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M. W. Shephard
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A. Velázquez-Blázquez
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U. Wandinger
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T. Wehr
, and
G.-J. van Zadelhoff

Abstract

The collective representation within global models of aerosol, cloud, precipitation, and their radiative properties remains unsatisfactory. They constitute the largest source of uncertainty in predictions of climatic change and hamper the ability of numerical weather prediction models to forecast high-impact weather events. The joint European Space Agency (ESA)–Japan Aerospace Exploration Agency (JAXA) Earth Clouds, Aerosol and Radiation Explorer (EarthCARE) satellite mission, scheduled for launch in 2018, will help to resolve these weaknesses by providing global profiles of cloud, aerosol, precipitation, and associated radiative properties inferred from a combination of measurements made by its collocated active and passive sensors. EarthCARE will improve our understanding of cloud and aerosol processes by extending the invaluable dataset acquired by the A-Train satellites CloudSat, Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), and Aqua. Specifically, EarthCARE’s cloud profiling radar, with 7 dB more sensitivity than CloudSat, will detect more thin clouds and its Doppler capability will provide novel information on convection, precipitating ice particle, and raindrop fall speeds. EarthCARE’s 355-nm high-spectral-resolution lidar will measure directly and accurately cloud and aerosol extinction and optical depth. Combining this with backscatter and polarization information should lead to an unprecedented ability to identify aerosol type. The multispectral imager will provide a context for, and the ability to construct, the cloud and aerosol distribution in 3D domains around the narrow 2D retrieved cross section. The consistency of the retrievals will be assessed to within a target of ±10 W m–2 on the (10 km)2 scale by comparing the multiview broadband radiometer observations to the top-of-atmosphere fluxes estimated by 3D radiative transfer models acting on retrieved 3D domains.

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