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H. Jean Thiebaux and Ranjit M. Passi

Abstract

Properties of the optimal combination scheme for several vector estimates are developed for correlated estimates having common ensemble mean. Classic optimality properties of a linear combination of estimates from separate sources are established as corollaries of the more general optimization criterion. simultaneous minimization of variance components.

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Ricardo M. Trigo and Jean P. Palutikof

Abstract

The Iberian rainfall regime is characterized by a strong seasonal cycle and large interannual variability. Typically, frequency distributions of monthly precipitation present a large spread of values, implying frequent episodes of very wet or very dry years. Unfortunately, the most recent generation of general circulation models (GCMs) still has serious problems when modeling monthly precipitation over southern Europe. However, these models are able to reproduce the main patterns of atmospheric circulation, such as those derived from a principal component analysis of the sea level pressure anomaly field. Many downscaling techniques have been developed in recent years, all having in common the need to establish statistical links between the large-scale circulation and the observed precipitation at a local or regional scale. The final objective is usually the application of such transfer functions to GCM output.

In this work, linear and nonlinear downscaling transfer functions are developed based on artificial neural networks (ANNs), to downscale monthly precipitation to nine grid boxes over the Iberian Peninsula. The nonlinear ANN models were run 20 times, with different initial conditions, in order to study the stability of the final results. All the models were developed on a seasonal basis, calibrated between 1901 and 1940 and validated between 1941 and 1990. It was found that linear or slightly nonlinear ANN models (with just one node in the first layer) were more capable of reproducing the observed precipitation than more complex nonlinear ANN models. GCM data from a greenhouse gas–plus-sulfates run from the Hadley Centre Model (HadCM2) were used to reproduce present-day precipitation over Iberia. It was found that the precipitation characteristics (mean, variance, and empirical distribution) were better reproduced by the downscaled results than by the GCM direct output. Precipitation scenarios constructed for the future (2041–90) reveal an increase of precipitation in winter and small decreases in most sectors of Iberia for the spring and autumn seasons. Such scenarios are in good agreement with those obtained by other researchers using different downscaling techniques with HadCM2 data.

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Satellite Meteorology

How it all Started, 50 Years Ago

W. Paul Menzel and Jean M. Phillips

Abstract

No Abstract available.

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Mark A. Askelson, Jean-Pierre Aubagnac, and Jerry M. Straka

Abstract

Spatial objective analysis is routinely performed in several applications that utilize radar data. Because of their relative simplicity and computational efficiency, one-pass distance-dependent weighted-average (DDWA) schemes that utilize either the Cressman or the Barnes filter are often used in these applications. The DDWA schemes that have traditionally been used do not, however, directly account for two fundamental characteristics of radar data. These are 1) the spacing of radar data depends on direction and 2) radar data density systematically decreases with increasing range.

A DDWA scheme based on an adaptation of the Barnes filter is proposed. This scheme, termed the adaptive Barnes (A-B) scheme, explicitly takes into account radar data properties 1 and 2 above. Both theoretical and experimental investigations indicate that two attributes of the A-B scheme, direction-splitting and automatic adaptation to data density, may facilitate the preservation of the maximum amount of meaningful information possible within the confines of one-pass DDWA schemes.

It is shown that in the idealized situation of infinite, continuous data and for an analysis in rectangular-Cartesian coordinates, a direction-splitting scheme does not induce phase shifts if the weight function is even in each direction. Moreover, for radar data that are infinite, collected at regular radial, azimuthal, and elevational increments, and collocated with analysis points, the direction-splitting design of the A-B filter removes gradients in the analysis weights. This is a beneficial attribute when considering the treatment of gradient information of rectangular Cartesian data by an analysis system because then postanalysis gradients equal the analysis of gradients. The direction-splitting design of the A-B filter is unable, however, to circumvent the impact of the varying physical distances between adjacent measurements that are inherent to the spherical coordinate system of ground-based weather radars. Because of this, even with the direction-splitting design of the A-B filter postanalysis gradients do not equal the analysis of gradients.

Ringing in the response function of a one-dimensional Barnes filter is illustrated. The negative impact of data windows on the main lobe of the response function is found to decrease rapidly as the window is widened relative to the weight function. Unless an analysis point is near a data boundary, in which case both ringing and phase shifting will adversely affect the analysis, window effects are unlikely to be significant in applications of the A-B filter to radar data.

The A-B filter has potential drawbacks, the most significant of which is misinterpretations owing to the use of the A-B filter without comprehension of its direction- and range-dependent response function. Despite its drawbacks, the A-B filter has the potential to improve analyses owing to the aforementioned attributes and thus to aid research efforts in areas such as multiple-Doppler wind analyses, pseudo-dual-Doppler analyses, and retrieval studies.

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Beatriz M. Funatsu, Chantal Claud, and Jean-Pierre Chaboureau

Abstract

A characterization of the large-scale environment associated with precipitating systems in the Mediterranean region, based mainly on NOAA-16 Advanced Microwave Sounding Unit (AMSU) observations from 2001 to 2007, is presented. Channels 5, 7, and 8 of AMSU-A are used to identify upper-level features, while a simple and tractable method, based on combinations of channels 3–5 of AMSU-B and insensitive to land–sea contrast, was used to identify precipitation. Rain occurrence is widespread over the Mediterranean in wintertime while reduced or short lived in the eastern part of the basin in summer. The location of convective precipitation shifts from mostly over land from April to August, to mostly over the sea from September to December. A composite analysis depicting large-scale conditions, for cases of either rain alone or extensive areas of deep convection, is performed for selected locations where the occurrence of intense rainfall was found to be important. In both cases, an upper-level trough is seen to the west of the target area, but for extreme rainfall the trough is narrower and has larger amplitude in all seasons. In general, these troughs are also deeper for extreme rainfall. Based on the European Centre for Medium-Range Weather Forecasts operational analyses, it was found that sea surface temperature anomalies composites for extreme rainfall are often about 1 K warmer, compared to nonconvective precipitation conditions, in the vicinity of the affected area, and the wind speed at 850 hPa is also stronger and usually coming from the sea.

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Jean-François Caron, M. K. Yau, Stéphane Laroche, and Peter Zwack

Abstract

This study examines a few approaches to isolate the balanced component of the initial corrections from the Canadian Meteorological Centre energy-norm-based key analysis error algorithm, in an attempt to capture the part of the key analysis errors responsible for short-range forecast errors. The best results were obtained with the nonlinear balance potential vorticity (PV) inversion technique. It was shown that the PV component of the initial corrections contains the essential information for reducing short-range forecast errors. The remaining imbalance part of the initial corrections does not grow in time and does not contribute to the improvement of the forecast. The removal of the imbalance part of the initial corrections makes the corrected analysis slightly closer to the observations, but remains systematically farther away as compared with the original analysis. Thus the balanced part of the key analysis errors cannot justifiably be associated to analysis errors. A methodology to balance the divergent part of the initial corrections, which reduces significantly the spinup in the vertical motion corrections, is also presented. Finally, in light of the results presented in this paper, some recommendations to improve the key analysis error algorithm are proposed.

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Jean-François Caron, M. K. Yau, and Stéphane Laroche

Abstract

This paper presents a diagnostic study of the evolution of initial corrections obtained from the key analysis error algorithm that minimizes the short-range (24 h) forecast errors for four specific events poorly forecasted over the eastern part of North America. A potential vorticity (PV) perspective is employed. It is shown that the modification to the low-level structure at the initial time is mainly attributed to the modification of the low-level PV distribution, while changes in the upper-level structure are attributed to the modification of the upper-level PV distribution. The low-level corrections grow mainly through background surface potential temperature advection by the wind corrections attributable to the interior PV corrections. Changes in the diabatic processes and the vertical alignment of low-level PV corrections by differential PV advection also increase the magnitude of the low-level corrections with time. The upper-level corrections grow by advection of background PV from wind corrections. However, the cause of these latter wind corrections responsible for upper-level background PV advection varies from case to case. An investigation of the relative importance of the low-level and of the upper-level initial corrections to produce the final-time corrections also reveals strong variability between cases. Finally, comparison of two cases in which the key analysis errors propagate vertically with two others without significant vertical propagation shows how the relative position of the key analysis errors with respect to the structure of the background flow can influence the evolution of the initial corrections.

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Jean-François Caron, M. K. Yau, Stéphane Laroche, and Peter Zwack

Abstract

The characteristics of the initial corrections obtained from the Canadian Meteorological Centre (CMC) energy-norm-based key analysis error algorithm that minimizes short-range (24 h) forecast errors were investigated for four specific CMC operational analyses. The results show that both the rotational and the divergent components of the initial corrections are strongly out of balance. Some dispersive modes are also present in the mass component of the initial corrections. The results from one experiment where the initial state errors were known suggest that the current algorithm always selects a set of unbalanced initial corrections with more mass correction than wind correction, regardless of the characteristics of the real initial condition errors. Comparison with observational data showed that the corrected analysis is systematically farther away from the observations than the control analysis even in large forecast error events where most of the forecast errors are believed to have originated from errors in the initial state.

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Ronald M. Errico, Peter Bauer, and Jean-François Mahfouf

Abstract

This is a reply to a set of criticisms regarding a previously published work. It briefly addresses the main criticisms. In particular, it explains why some papers identified as having some fundamental flaws were referenced in the original work without detailed exposition of those flaws. It also explains why parts of the conclusions criticized as being contradictory are, in fact, not. It further highlights the need for more publishing of scientific criticisms.

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Peter A. E. M. Janssen, Björn Hansen, and Jean-Raymond Bidlot

Abstract

The present status of ocean wave modeling at the European Centre for Medium-Range Weather Forecasts (ECMWF) is reviewed. Ocean waves are forecasted globally up to 10 days by means of the Wave Model (WAM), which is driven by 10-m winds from the ECMWF atmospheric model. Initial conditions are provided by assimilation of ERS-1 data into the first-guess wave field.

The analyzed wave height and peak period field are verified against buoy data and show a considerable improvement compared to verification results of a decade ago. This is confirmed by a comparison of first-guess wave height against ERS-1 altimeter data. The main reasons for this improvement are (i) the higher quality of ECMWF winds compared to a decade ago, (ii) the improved physics of the WAM model, and (iii) the assimilation of ERS-1 data.

The forecast skill of the ECMWF wave forecasting system is also studied by comparing forecasts with buoy data and verifying analysis. Error growth in forecast wave height is less rapid than in forecast wind speed. However, considerable positive mean errors in forecast wave height are found, suggesting a too active atmospheric model in later stages of the forecast. Nevertheless, judging from anomaly correlation scores, the wave forecast seems to be useful up to day 5 in the forecast in the Northern Hemisphere. Since the wave forecast depends in a sensitive manner on the wind forecast, this confirms the high quality of ECMWF forecasts near the surface.

Finally, promising ways of improving the wave forecast are also discussed, and, as an example, the positive impact three-dimensional variational assimilation in the atmospheric model has on the wave product is also mentioned.

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