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Marina Živković and Jean-François Louis

Abstract

In the present paper, we review a new method for relating cloud observations to large-scale variables of general circulation models. The method is based on an application of the cluster analysis to synoptic analyses Of prognostic model variables provided by the National Meteorological Center. Surface cloud observations are “clustered” according to the similarity of the principal-component loading scores of the corresponding vertical soundings. The method was tested by developing a simple cloud parameterization scheme, from the cluster-stratified cloud data, and comparing it with the observations.

Parameterization results are compared qualitatively against satellite imagery and surface analysis, and quantitatively against a scheme based on one variable only, the relative humidity. Qualitative comparison shows that the new approach generates cloud parameterization consistent with observations, especially with cloud structures related to various synoptic-scale flows. Quantitative comparisons indicate a possible advantage of the present method in the areas covered by limited observations. Overall, the results are suggestive of a possible alternative for upgrading and validating cloud schemes presently used in general circulation models.

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Jure Cedilnik, Dominique Carrer, Jean-François Mahfouf, and Jean-Louis Roujean

Abstract

This study examines the impact of daily satellite-derived albedos on short-range forecasts in a limited-area numerical weather prediction (NWP) model over Europe. Contrary to previous studies in which satellite products were used to derive monthly “climatologies,” a daily surface (snow free) albedo is analyzed by a Kalman filter. The filter combines optimally a satellite product derived from the Meteosat Second Generation geostationary satellite [and produced by the Land Surface Analyses–Satellite Application Facility of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)], an albedo climatology, and a priori information given by “persistence.” The surface albedo analyzed for a given day is used as boundary conditions of the NWP model to run forecasts starting the following day. Results from short-range forecasts over a 1-yr period reveal the capacity of satellite information to reduce model biases and RMSE in screen-level temperature (during daytime and intermediate seasons). The impact on forecast scores is larger when considering the analyzed surface albedo rather than another climatologically based albedo product. From comparisons with measurements from three flux-tower stations over mostly homogeneous French forests, it is seen that the model biases in surface net radiation are significantly reduced. An impact on the whole planetary boundary layer, particularly in summer, results from the use of an observed surface albedo. An unexpected behavior produced in summer by the satellite-derived albedo on surface temperature is also explained. The forecast runs presented here, performed in dynamical adaptation mode, will be complemented later on by data assimilation experiments over typically monthly periods.

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Ross N. Hoffman, Zheng Liu, Jean-Francois Louis, and Christopher Grassoti

Abstract

Forecast error is decomposed into three components, termed displacement error, amplitude error, mid residual error, respectively. Displacement error measures how much of the forecast error can be accounted for by moving the forecast to best fit the analysis. Amplitude error measures how much of the forecast error can be accounted for by changing the amplitude of the displaced forecast to best fit the analysis. The combination of a displacement and an amplification is called a distortion. The part of the forecast error unaccounted for by the distortion is called the residual error. The distortion must be large scale, in line with the basic premise that forecast errors are best described by reference to large-scale meteorological features. A general mathematical formalism for defining distortions and decomposing forecast errors into distortion and residual errors is formulated. The distortion representation of forecast errors should prove useful for describing forecast skill and for representing the statistics of the background errors in objective data analysis.

Examples using nonstandard satellite data–SSM/I precipitable water and ERS-1 backscatter—demonstrate the detection and characterization of analysis errors in terms of position mid amplitude errors. In addition, a 48-h forecast of Northern Hemisphere 500-hPa geopotential height is decomposed. For this case a large-scale distortion is capable of representing the larger part of the forecast error field and the displacement error is predominant over the amplification error. These examples indicate the feasibility of implementing the proposed method in an operational setting.

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Thomas Nehrkorn, Ross N. Hoffman, Jean-François Louis, Ronald G. Isaacs, and Jean-Luc Moncet

Abstract

The potential improvements of analyses and forecasts from the use of satellite-observed rainfall and water vapor measurements from the Defense Meteorological Satellite Program Special Sensor Microwave (SSM) T-1 and T-2 instruments are investigated in a series of observing system simulation experiments using the Air Force Phillips Laboratory (formerly Air Force Geophysics Laboratory) data assimilation system. Simulated SSM radiances are used directly in a radiance retrieval step following the conventional optimum interpolation analysis. Simulated rainfall rates in the tropics are used in a moist initialization procedure to improve the initial specification of divergence, moisture, and temperature.

Results show improved analyses and forecasts of relative humidity and winds compared to the control experiment in the tropics and the Southern Hemisphere. Forecast improvements are generally restricted to the first 1–3 days of the forecast.

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Christopher Grassotti, S. Mark Leidner, Jean-François Louis, and Ross N. Hoffman

Abstract

The authors report on characteristics of a rain flag derived from collocation of visible and infrared image data with rain rates over the North Atlantic Ocean obtained from microwave imagery (SSM/I) during a 3-week period (15 October 1996–2 November 1996). The rain flag has been developed as part of an effort to provide an indication of contamination by heavy rainfall in NASA scatterometer datasets. The primary results of this analysis indicate 1) that a simple albedo/infrared brightness temperature threshold is capable of flagging most of the heavy rainfall, though with a fairly high rate of false alarms, and 2) that the small difference in optimal threshold between the Tropics and midlatitudes can probably be ignored. Use of the rain flag in 12 assimilation experiments during this period showed that the number of rain-flagged wind vector cells is generally less than 1% of the number of cells. Overall, the impact from using the rain-flagged data is generally less than 5 m s−1 and localized (less than 5° of latitude and longitude). However, in some cases, the effect of excluding just one to five rain-flagged points can change the resulting analysis significantly, because their placement is critical for defining the flow along a front or some other shear-dominated environment.

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Ross N. Hoffman, Christopher Grassotti, Ronald G. Isaacs, Jean-Francois Louis, Thomas Nehrkorn, and Donald C. Norquist

Abstract

A series of observing system simulation experiments (0SSEs) was conducted to assess the impacts on the Air Force Geophysics Laboratory (GL) global data assimilation system (GDAS) of a satellite Doppler wind lidar sounding system (WINDSAT) and of the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave (SSM) T-1 and T-2 temperature and moisture retrievals. (The SSM/T-2 is expected to be launched in the early 1990s.) In simulating the SSM data, some horizontal correlations were induced because the simulated errors had different biases in different geophysical regimes. As an interpretative aid we calibrated our results to a series of real data experiments.

In an experiment in which the WINDSAT data is added to the observational database, the analyses and forecasts are improved relative to the control experiment. These improvements are large in the Southern Hemisphere extratropics. The addition of the SSM data improves the analysis of moisture particularly in the tropics and Southern Hemisphere extratropics.

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David Antoine, Pierre Guevel, Jean-François Desté, Guislain Bécu, Francis Louis, Alec J. Scott, and Philippe Bardey

Abstract

A new concept of oceanographic data buoy is described, which couples a taut mooring and a “transparent-to-swell” superstructure, and is specifically designed for the collection of radiometric quantities in offshore environments. The design of the thin superstructure addresses two major requirements: stabilizing the instruments in the water column and avoiding shading them. The development of the buoy is described, starting with the theoretical assessment and then describing the various stages of development leading to the latest version of the mooring and buoy. Its performance at sea is also analyzed. This new platform has been deployed in the deep waters (>2400 m) of the northwestern Mediterranean Sea for about 4 yr (since September 2003) and provides a quasi-continuous record of optical properties at this site. The data are used for bio-optics research and for calibration and validation operations of several European and U.S. ocean color satellite missions. The plan is to continue the deployment to build a decadal time series of optical properties. The instrument suite that is installed on this buoy is also briefly described, and sample results are shown to demonstrate the ability of this new system to collect the data at the desired frequency and quality.

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Armel Thibaut Kaptué Tchuenté, Jean-Louis Roujean, Agnès Bégué, Sietse O. Los, Aaron A. Boone, Jean-François Mahfouf, Dominique Carrer, and Badiane Daouda

Abstract

Information related to land surface is immensely important to global change science. For example, land surface changes can alter regional climate through its effects on fluxes of water, energy, and carbon. In the past decades, data sources and methodologies for characterizing land surface heterogeneity (e.g., land cover, leaf area index, fractional vegetation cover, bare soil, and vegetation albedos) from remote sensing have evolved rapidly. The double ECOCLIMAP database—constituted of a land cover map and land surface variables and derived from Advanced Very High Resolution Radiometer (AVHRR) observations acquired between April 1992 and March 1993—was developed to support investigations that require information related to spatiotemporal dynamics of land surface. Here is the description of ECOCLIMAP-II: a new characterization of the land surface heterogeneity based on the latest generation of sensors, which represents an update of the ECOCLIMAP-I database over Africa. Owing to the many features of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors (more accurate in spatial resolution and spectral information compared to the AVHRR sensor), a variety of methods have been developed for an extended period of 8 yr (2000–07) to strengthen consistency between land surface variables as required by the meteorological and ecological communities. The relative accuracy (or performance) quality of ECOCLIMAP-II was assessed (i.e., by comparison with other global datasets). Results illustrate a substantial refinement; for instance, the fractional vegetation cover resulting in a root-mean-square error of 34% instead of 64% in comparison with the original version of ECOCLIMAP.

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Florence Rabier, Steve Cohn, Philippe Cocquerez, Albert Hertzog, Linnea Avallone, Terry Deshler, Jennifer Haase, Terry Hock, Alexis Doerenbecher, Junhong Wang, Vincent Guidard, Jean-Noël Thépaut, Rolf Langland, Andrew Tangborn, Gianpaolo Balsamo, Eric Brun, David Parsons, Jérôme Bordereau, Carla Cardinali, François Danis, Jean-Pierre Escarnot, Nadia Fourrié, Ron Gelaro, Christophe Genthon, Kayo Ide, Lars Kalnajs, Charlie Martin, Louis-François Meunier, Jean-Marc Nicot, Tuuli Perttula, Nicholas Potts, Patrick Ragazzo, David Richardson, Sergio Sosa-Sesma, and André Vargas
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