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Peter Lean, Stefano Migliorini, and Graeme Kelly

Abstract

Atmospheric motion vectors (AMVs) have been produced for decades and remain an important source of wind information. Many studies have suggested that the traditional interpretation of AMVs as representative of the wind at cloud top is suboptimal and that they are more representative of the winds within the cloud. This paper investigates the vertical representativity of cloudy AMVs using both first-guess departure [observation − background (OB)] statistics and the simulation-study technique. A state-of-the-art convection-permitting mesoscale model (“UKV”) is used in conjunction with a radiative transfer model and the Nowcasting Satellite Application Facility (NWCSAF) AMV package to produce synthetic AMVs over a 1-month period. The simulated upper-level AMVs suffered from large height-assignment errors uncharacteristic of those in reality; these issues were partially alleviated by using the model cloud top instead of the assigned height. In agreement with previous studies, both the simulated and real AMVs were found to have the closest fit to a layer mean of the model winds with the majority of the layer below the estimated cloud top. However, improvements in the fit between the AMVs and the model were also found by simply lowering the assigned height. A short NWP trial hinted that height reassignment might lead to short-range forecast improvements. The results of this study indicate that the simulation technique was able to match the usefulness of OB statistics for AMVs associated with low- and medium-level clouds (albeit at a higher computational cost); however, challenges remain in the simulation of upper-level clouds.

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Niels Bormann, Jean-Noël Thépaut, and Graeme Kelly

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This study examines atmospheric motion vectors derived by tracking features in image sequences from Meteosat for anomalies during the second satellite eclipse period of 2001. During eclipse periods (March–April; September–October), data from geostationary sensors are prone to anomalies caused by solar stray light entering the radiometer around local midnight. As a result, imagery from geostationary satellites can exhibit local anomalies for certain time slots, and these anomalies can change considerably from one time slot to the next. The effect of these anomalies on atmospheric motion vectors is characterized in this study by investigating the temporal consistency of the data and by monitoring the winds against short-term forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) global model. Atmospheric motion vectors from the Meteosat water vapor channel exhibit spuriously fast winds for certain time slots around local midnight for extended periods around the satellite's eclipse. The anomalies are caused primarily through tracking problems, but height assignment is also affected. Less severe anomalies are found for infrared winds, in agreement with less severe image anomalies for the infrared channel. The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) and ECMWF quality control are capable of reducing the problem, but anomalous characteristics can still be found in the quality-controlled subsamples. As a result of these findings, atmospheric motion vectors from Meteosat are now excluded from the operational assimilation at ECMWF for certain time slots around the eclipse periods.

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Michael Rohn, Graeme Kelly, and Roger W. Saunders

Abstract

Enhanced wind datasets of the European satellite Meteosat are now provided every 90 mins together with the quality indicator (QI) derived by the quality control of the Meteorological Product Extraction Facility (MPEF) at the European Organisation for the Exploitation of Meteorological Satellites. All three channel cloud motion winds and clear sky water vapor motion winds have been passively monitored by comparison with the European Centre for Medium-Range Weather Forecasts model background field. The evaluation of the relationship between the MPEF QI and the observation − background differences indicate possible benefits to be gained from the use of the QI within the observation screening of the assimilation system. The MPEF quality indicator is used as a selection criterion within the screening. The applied thresholds are restricted in the Tropics compared to the extratropical regions where the threshold for high-level winds has been relaxed below the automatic quality control at MPEF. The wind data derived from imagery of both Meteosat platforms at 0° and 118°E are used in this study. The overall effect is an increase of active Meteosat winds by a factor of 2. This means a considerably increased impact of Meteosat winds on the tropospheric analyses. The assessment of mean wind increments indicates that the increased temporal sampling together with the use of the quality indicator within the observation screening leads to an improvement of the consistency of the atmospheric motion wind data actively used within the four-dimensional variational assimilation system. The averaged impact on the short- and medium-range forecasts is found to be neutral in the Northern Hemisphere and positive in the Southern Hemisphere. In a selected synoptic case study the use of the new Meteosat wind product indicates a considerable improvement of the medium-range forecasts for the North Atlantic and European areas.

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William A. Heckley, Graeme Kelly, and Michael Tiedtke

Abstract

The HIRS instrument on the NOAA polar orbiting satellites is used to obtain coverage of outward-going longwave radiation across the global tropics and subtropics four times a day. Fractional coverage of cold cloud is obtained from this, which is then interpreted as rainfall rates. This information is introduced into the ECMWF data assimilation system as diabatic heating through the nonlinear normal-mode initialization scheme. Substantial impact on the analyses is found.

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Niels Bormann, Sami Saarinen, Graeme Kelly, and Jean-Noël Thépaut

Abstract

This study investigates and quantifies in detail the spatial correlations of random errors in atmospheric motion vectors (AMVs) derived by tracking structures in imagery from geostationary satellites. A good specification of the observation error is essential to assimilate any kind of observation for numerical weather prediction in a near-optimal way. For AMVs, height assignment, tracking of similar cloud structures, or quality control procedures may introduce spatially correlated errors.

The spatial structure of the error correlations is investigated based on a 1-yr dataset of pairs of collocations between AMVs and radiosonde observations. Assuming spatially uncorrelated sonde errors, the spatial AMV error correlations are obtained over dense sonde networks. Results for operational infrared and water vapor wind datasets from Meteosat-5 and -7, Geostationary Operational Environmental Satellite-8 and -10 (GOES-8 and -10), and Geostationary Meteorological Satellite-5 (GMS-5) are presented.

Winds from all five datasets show statistically significant spatial error correlations for distances up to about 800 km, with little difference between satellites, channels, or vertical levels. Even broader correlations are found for tropical regions. The correlations exhibit considerable anisotropic structures with, for instance, longer correlation scales in the south–north direction for the υ-wind component, and are comparable to error correlations for short-term forecasts. The study estimates the spatially correlated part of the annual mean AMV wind component error for high-level Northern Hemisphere winds to be about 2.7–3.5 m s−1. Some seasonal variation is found for these errors with larger values in winter. The findings have a number of important implications for the use of AMVs in data assimilation.

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Catherine Prigent, Frédéric Chevallier, Fatima Karbou, Peter Bauer, and Graeme Kelly

Abstract

This study describes the work performed at the European Centre for Medium-Range Weather Forecasts (ECMWF) to estimate the microwave land surface emissivities at Advanced Microwave Sounding Unit (AMSU)-A frequencies within the specific context and constraint of operational assimilation. The emissivities are directly calculated from the satellite observations in clear-sky conditions using the surface skin temperature derived from ECMWF and the Radiative Transfer for the Television and Infrared Observation Satellite Operational Vertical Sounder (RTTOVS) model, along with the forecast model variables to estimate the atmospheric contributions. The results are analyzed, with special emphasis on the evaluation of the frequency and angular dependencies of the emissivities with respect to the surface characteristics. Possible extrapolation of the Special Sensor Microwave Imager (SSM/I) emissivities to those of the AMSU is considered. Direct calculation results are also compared with emissivity model outputs.

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Adrian Simmons, Mariano Hortal, Graeme Kelly, Anthony McNally, Agathe Untch, and Sakari Uppala

Abstract

Breakup of the polar stratospheric vortex in the Northern Hemisphere is an event that is known to be predictable for up to a week or so ahead. This is illustrated using data from the 45-yr ECMWF Re-Analysis (ERA-40) for the sudden warmings of January 1958 and February 1979 and operational ECMWF data for February 2003. It is then shown that a similar level of skill was achieved in operational forecasts for the split of the southern stratospheric vortex in late September 2002. The highly unusual flow conditions nevertheless exposed a computational instability of the forecast model. Analyses and forecasts from reruns using improved versions of the forecasting system are presented. Isentropic maps of potential vorticity and specific humidity provide striking pictures of the advective processes at work. Forecasts as well as analyses are shown to be in good agreement with radiosonde measurements of the temperature changes associated with vortex movement, distortion, and breakup during August and September. Forecasts from 17 September onward capture the remarkable temperature rise of about 60°C recorded at 20 hPa by the Halley radiosonde station as the vortex split. Objective forecast verification and data denial experiments are used to characterize the performance of the observing and data assimilation systems and to infer overall forecast, analysis, and observation accuracy. The observations and analyses from 1957 onward in the ERA-40 archive confirm the extreme nature of the 2002 event. Secondary vortex development by barotropic instability is also discussed; in analyses for early October 2002, the process is active in the breakup of the weaker of the two vortices formed by the late-September split.

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Frédéric Chevallier, Graeme Kelly, Adrian J. Simmons, Sakari Uppala, and Angeles Hernandez

Abstract

The reanalysis programs of numerical weather prediction (NWP) centers provide global, comprehensive descriptions of the atmosphere and of the earth’s surface over long periods of time. The high realism of their representation of key NWP parameters, like temperature and winds, implies some realism for less emblematic parameters, such as cloud cover, but the degree of this realism needs to be documented.

This study aims to evaluate the high clouds over open oceans in the ECMWF 15- and 45-yr reanalyses. The assessment is based on a new 23-yr climatology of monthly frequencies of high-cloud occurrence retrieved from the infrared radiances measured by operational polar satellites. It is complemented by data from the International Satellite Cloud Climatology Project.

It is shown that the 45-yr ECMWF reanalysis dramatically improves on the previous 15-yr reanalysis for the realism of seasonal and interannual variations in high clouds, despite remaining systematic errors. More than 60% of the observed anomalies during the January 1979–February 2002 period over large oceanic basins are captured by the latest reanalysis. However the realism of the analyses in the areas and in the years with sparse observations appears to be poor. Consequently, the interannual variations may not be reliable before January 1979 in most parts of the world. Possible improvements of the handling of assimilated satellite observations before and after this date are suggested.

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Erik Andersson, Peter Bauer, Anton Beljaars, Frederic Chevallier, Elías Hólm, Marta Janisková, Per Kållberg, Graeme Kelly, Philippe Lopez, Anthony McNally, Emmanuel Moreau, Adrian J. Simmons, Jean-Noël Thépaut, and Adrian M. Tompkins

Several new types of satellite instrument will provide improved measurements of Earth's hydrological cycle and the humidity of the atmosphere. In an effort to make the best possible use of these data, the modeling and assimilation of humidity, clouds, and precipitation are currently the subjects of a comprehensive research program at the European Centre for Medium-Range Weather Forecasts (ECMWF). Impacts on weather prediction and climate reanalysis can be expected. The preparations for cloud and rain assimilation within ECMWF's four-dimensional variational data assimilation system include the development of linearized moist physics, the development of fast radiative transfer codes for cloudy and precipitating conditions, and a reformulation of the humidity analysis scheme.

Results of model validations against in situ moisture data are presented, indicating generally good agreement—often to within the absolute calibration accuracy of the measurements. Evidence is also presented of shortcomings in ECMWF's humidity analysis, from the operational data assimilation and forecasting system in 2002, and from the recently completed ERA-40 reanalysis project. Examples are shown of biases in the data and in the model that lead to biased humidity analyses. Although these biases are relatively small, they contribute to an overprediction of tropical precipitation and to an overly intense Hadley circulation at the start of the forecast, with rapid adjustments taking place during the first 6–12 h. It is shown that with an improved humidity analysis this long-standing “spindown” problem can be reduced.

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David C. Leon, Jeffrey R. French, Sonia Lasher-Trapp, Alan M. Blyth, Steven J. Abel, Susan Ballard, Andrew Barrett, Lindsay J. Bennett, Keith Bower, Barbara Brooks, Phil Brown, Cristina Charlton-Perez, Thomas Choularton, Peter Clark, Chris Collier, Jonathan Crosier, Zhiqiang Cui, Seonaid Dey, David Dufton, Chloe Eagle, Michael J. Flynn, Martin Gallagher, Carol Halliwell, Kirsty Hanley, Lee Hawkness-Smith, Yahui Huang, Graeme Kelly, Malcolm Kitchen, Alexei Korolev, Humphrey Lean, Zixia Liu, John Marsham, Daniel Moser, John Nicol, Emily G. Norton, David Plummer, Jeremy Price, Hugo Ricketts, Nigel Roberts, Phil D. Rosenberg, David Simonin, Jonathan W. Taylor, Robert Warren, Paul I. Williams, and Gillian Young

Abstract

The Convective Precipitation Experiment (COPE) was a joint U.K.–U.S. field campaign held during the summer of 2013 in the southwest peninsula of England, designed to study convective clouds that produce heavy rain leading to flash floods. The clouds form along convergence lines that develop regularly as a result of the topography. Major flash floods have occurred in the past, most famously at Boscastle in 2004. It has been suggested that much of the rain was produced by warm rain processes, similar to some flash floods that have occurred in the United States. The overarching goal of COPE is to improve quantitative convective precipitation forecasting by understanding the interactions of the cloud microphysics and dynamics and thereby to improve numerical weather prediction (NWP) model skill for forecasts of flash floods. Two research aircraft, the University of Wyoming King Air and the U.K. BAe 146, obtained detailed in situ and remote sensing measurements in, around, and below storms on several days. A new fast-scanning X-band dual-polarization Doppler radar made 360° volume scans over 10 elevation angles approximately every 5 min and was augmented by two Met Office C-band radars and the Chilbolton S-band radar. Detailed aerosol measurements were made on the aircraft and on the ground. This paper i) provides an overview of the COPE field campaign and the resulting dataset, ii) presents examples of heavy convective rainfall in clouds containing ice and also in relatively shallow clouds through the warm rain process alone, and iii) explains how COPE data will be used to improve high-resolution NWP models for operational use.

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