Numerical Weather Prediction (numerical + weather_prediction)

Distribution by Scientific Domains

Terms modified by Numerical Weather Prediction

  • numerical weather prediction model
  • numerical weather prediction models

  • Selected Abstracts

    Modeling and predicting complex space,time structures and patterns of coastal wind fields

    ENVIRONMETRICS, Issue 5 2005
    Montserrat Fuentes
    Abstract A statistical technique is developed for wind field mapping that can be used to improve either the assimilation of surface wind observations into a model initial field or the accuracy of post-processing algorithms run on meteorological model output. The observed wind field at any particular location is treated as a function of the true (but unknown) wind and measurement error. The wind field from numerical weather prediction models is treated as a function of a linear and multiplicative bias and a term which represents random deviations with respect to the true wind process. A Bayesian approach is taken to provide information about the true underlying wind field, which is modeled as a stochastic process with a non-stationary and non-separable covariance. The method is applied to forecast wind fields from a widely used mesoscale numerical weather prediction (NWP) model (MM5). The statistical model tests are carried out for the wind speed over the Chesapeake Bay and the surrounding region for 21 July 2002. Coastal wind observations that have not been used in the MM5 initial conditions or forecasts are used in conjunction with the MM5 forecast wind field (valid at the same time that the observations were available) in a post-processing technique that combined these two sources of information to predict the true wind field. Based on the mean square error, this procedure provides a substantial correction to the MM5 wind field forecast over the Chesapeake Bay region. Copyright © 2005 John Wiley & Sons, Ltd. [source]

    A 10 year cloud climatology over Scandinavia derived from NOAA Advanced Very High Resolution Radiometer imagery

    Karl-Göran Karlsson
    Abstract Results from a satellite-based method to compile regional cloud climatologies covering the Scandinavian region are presented. Systematic processing of multispectral image data from the NOAA Advanced Very High Resolution Radiometer (AVHRR) instrument has been utilized to provide monthly cloud climatologies covering the period 1991,2000. Considerable local-scale variation of cloud amounts was found in the region. The inland Baltic Sea and adjacent land areas exhibited a large-amplitude annual cycle in cloudiness (high cloud amounts in winter, low cloud amounts in summer) whereas a weak-amplitude reversed annual cycle (high cloud amounts with a weak maximum in summer) was found for the Scandinavian mountain range. As a contrast, conditions over the Norwegian Sea showed high and almost unchanged cloud amounts during the course of the year. Some interesting exceptions to these patterns were also seen locally. The quality of the satellite-derived cloud climatology was examined through comparisons with climatologies derived from surface cloud observations, from the International Satellite Cloud Climatology Project (ISCCP) and from the European Centre for Medium-range Weather Forecasts ERA-40 data set. In general, cloud amount deviations from surface observations were smaller than 10% except for some individual winter months, when the separability between clouds and snow-covered cold land surfaces is often poor. The ISCCP data set showed a weaker annual cycle in cloudiness, generally caused by higher summer-time cloud amounts in the region. Very good agreement was found with the ERA-40 data set, especially for the summer season. However, ERA-40 showed higher cloud amounts than SCANDIA and ISCCP during the winter season. The derived cloud climatology is affected by errors due to temporal AVHRR sensor degradation, but they appear to be small for this particular study. The data set is proposed as a valuable data set for validation of cloud description in numerical weather prediction and regional climate simulation models. Copyright © 2003 Royal Meteorological Society [source]

    Assessing the spatial and temporal variation in the skill of precipitation forecasts from an NWP model

    Nigel Roberts
    Abstract It is becoming increasingly important to be able to verify the spatial accuracy of precipitation forecasts, especially with the advent of high-resolution numerical weather prediction (NWP) models. In this article, the fractions skill score (FSS) approach has been used to perform a scale-selective evaluation of precipitation forecasts during 2003 from the Met Office mesoscale model (12 km grid length). The investigation shows how skill varies with spatial scale, the scales over which the data assimilation (DA) adds most skill, and how the loss of that skill is dependent on both the spatial scale and the rainfall coverage being examined. Although these results come from a specific model, they demonstrate how this verification approach can provide a quantitative assessment of the spatial behaviour of new finer-resolution models and DA techniques. Copyright © 2008 Royal Meteorological Society [source]

    Assimilation of radar reflectivity into the LM COSMO model with a high horizontal resolution

    Z. Sokol
    Abstract An assimilation of radar reflectivity into a numerical weather prediction (NWP) model with a horizontal resolution of 2.8 km is presented and applied to three severe convective events. The suggested assimilation method takes into account differences between the model and radar-derived precipitation in modifying vertical profiles of water vapour mixing ratio in each model time step by the nudging approach. Version 3.9 of the LM COSMO (Local Model COSMO) ,NWP model used in this study includes the explicit formulation of the cloud and rain processes involved. Two variants of the assimilation technique are designed and outputs of their implementation are compared. The first variant makes use of the ground data only, while the second utilises vertical profiles of precipitation water. Both variants provide an improvement of precipitation forecast in comparison with outputs of the control run without assimilation procedures applied. When the assimilated radar data indicate initial precipitation near an expected storm, the NWP model is capable of forecasting basic features of the storm development two to three hours ahead. Three case studies are presented. In one, the assimilation method that takes into account the vertical structure of the precipitation water yields better results than the others which utilise ground data only. However, for the remaining two case studies both types of the assimilation method produce comparable results. Copyright © 2006 John Wiley & Sons, Ltd. [source]

    Numerical prediction of severe convection: comparison with operational forecasts

    Milton S. Speer
    The prediction of severe convection is a major forecasting problem in Australia during the summer months. In particular, severe convection in the Sydney basin frequently produces heavy rain or hail, flash flooding, and destructive winds. Convective activity is a forecasting challenge for the Sydney basin, mainly from October to April. Currently, there is a need for improved numerical model guidance to supplement the official probabilistic convective outlooks, issued by the operational forecasters. In this study we assess the performance of a very high resolution (2 km) numerical weather prediction (NWP) model in terms of how well it performed in providing guidance on heavy rainfall and hail, as well as other mesoscale features such as low level convergence lines. Two cases are described in which the operational forecasts were incorrect on both occasions. Non-severe thunderstorms were predicted on 1 December 2000 but severe convection occurred. Severe convection was predicted on 8 December 2000, but no convection was reported. In contrast, the numerical model performed well, accurately predicting severe convection on 1 December and no convection on 8 December. These results have encouraged a program aimed at providing an enhanced numerical modelling capability to the operational forecasters for the Sydney basin. Copyright © 2003 Royal Meteorological Society [source]

    Statistical interpretation of NWP products in India

    Parvinder Maini
    Although numerical weather prediction (NWP) models provide an objective forecast, poor representation of local topography and other features in these models, necessitates statistical interpretation (SI) of NWP products in terms of local weather. The Perfect Prognostic Method (PPM) is one of the techniques for accomplishing this. At the National Center for Medium Range Weather Forecasting, PPM models for precipitation (quantitative, probability, yes/no) and maximum/minimum temperatures are developed for monsoon season by using analyses from the European Centre for Medium-Range Weather Forecasts. The SI forecast is then obtained by using these PPM models and output from the operational NWP model at the Center. Direct model output (DMO) obtained from the NWP model and the SI forecast are verified against the actual observations. The present study shows the verification scores obtained during the 1997 monsoon season for 10 locations in India. The results show that the SI forecast has good skill and is an improvement over DMO. Copyright © 2002 Royal Meteorological Society. [source]

    Modeling and Simulation of Notional Future Radar in Non-Standard Propagation Environments Facilitated by Mesoscale Numerical Weather Prediction Modeling

    Normal near surface radio-frequency (RF) propagation in the littorals across the land,sea boundary is rare due to the land,sea temperature difference, coastline shape, ground cover, urban density, coastal topography, and soil moisture content. The resulting frequent existence of coastal non-standard vertical profiles of refractivity and the resulting RF propagation has a profound impact on the performance of Navy ship-borne radars operating within 100 nm of the shore. In addition, these non-standard RF propagation conditions are spatio-temporally inhomogeneous. These spatial and time dependent propagation conditions and the resulting radar engineering implications cannot be revealed by a single vertical profile of refractivity taken near the ship borne radar. The results from single profile analysis techniques provide no spatiotemporal information and may lead to overly conservative radar design. Mesoscale numerical weather prediction (NWP) is a rapidly maturing technology with a strong operational Navy history that can provide a vertical profile of refractivity every 1 km in the battle space and every hour, up to 48 h, in the future. The Sensor Division at NSWCDD has applied mesoscale NWP for the last 2 years to better understand the performance of prototype radar in realistic four-dimensional (4D) coastal environments. Modern RF parabolic equation models are designed to model specific radar designs and to employ 3D refractivity fields from mesoscale NWP models. This allows for a radar design to be tested in realistic littoral non-standard atmospheres produced by stable internal boundary layers, sea breeze events, and the more rare sub-refractive events. Mesoscale NWP is currently qualitative for this purpose, but a research and development program focused on sea testing of prototype radars is described with the purpose of developing a more quantitative mesoscale NWP technology to support radar acquisition, testing, and operations. [source]

    Estimates of spatial and interchannel observation-error characteristics for current sounder radiances for numerical weather prediction.

    I: Methods, application to ATOVS data
    Abstract This is the first part of a two-part article that uses three methods to estimate observation errors and their correlations for clear-sky sounder radiances used in the European Centre for Medium-Range Weather Forecasts (ECMWF) assimilation system. The analysis is based on covariances derived from pairs of first-guess and analysis departures. The methods used are the so-called Hollingsworth/Lönnberg method, a method based on subtracting a scaled version of mapped assumed background errors from first-guess departure covariances and the Desroziers diagnostic. The present article reports the results for the three Advanced TIROS Operational Vertical Sounder (ATOVS) instruments: the Advanced Microwave Sounding Unit (AMSU)-A, High-Resolution Infrared Radiation Sounder (HIRS) and Microwave Humidity Sounder (MHS). The findings suggest that all AMSU-A sounding channels show little or no interchannel or spatial observation-error correlations, except for surface-sensitive channels over land. Estimates for the observation error are mostly close to the instrument noise. In contrast, HIRS temperature-sounding channels exhibit some interchannel error correlations, and these are stronger for surface-sensitive channels. There are also indications for stronger spatial-error correlations for the HIRS short-wave channels. There is good agreement between the estimates from the three methods for temperature-sounding channels. Estimating observation errors for humidity-sounding channels of MHS and HIRS appears more difficult. A considerable proportion of the observation error for humidity-sounding channels appears correlated spatially for short separation distances, as well as between channels. Observation error estimates for humidity channels are generally considerably larger than the instrument noise. Observation error estimates from this study are consistently lower than those assumed in the ECMWF assimilation system. As error correlations are small for AMSU-A, the study suggests that the current use of AMSU-A data in the ECMWF system in terms of observation-error or thinning-scale choices is fairly conservative. Copyright © 2010 Royal Meteorological Society [source]

    Ensemble data assimilation with the CNMCA regional forecasting system

    Massimo Bonavita
    Abstract The Ensemble Kalman Filter (EnKF) is likely to become a viable alternative to variational methods for the next generation of meteorological and oceanographic data assimilation systems. In this work we present results from real-data assimilation experiments using the CNMCA regional numerical weather prediction (NWP) forecasting system and compare them to the currently operational variational-based analysis. The set of observations used is the same as the one ingested in the operational data stream, with the exception of satellite radiances and scatterometer winds. Results show that the EnKF-based assimilation cycle is capable of producing analyses and forecasts of consistently superior skill in the root mean square error metric than CNMCA operational 3D-Var. One of the most important issues in EnKF implementations lies in the filter tendency to become underdispersive for practical ensemble sizes. To combat this problem a number of different parametrizations of the model error unaccounted for in the assimilation cycle have been proposed. In the CNMCA system a combination of adaptive multiplicative and additive background covariance inflations has been found to give adequate results and to be capable of avoiding filter divergence in extended assimilation trials. The additive component of the covariance inflation has been implemented through the use of scaled forecast differences. Following suggestions that ensemble square-root filters can violate the gaussianity assumption when used with nonlinear prognostic models, the statistical distribution of the forecast and analysis ensembles has been studied. No sign of the ensemble collapsing onto one or a few model states has been found, and the forecast and analysis ensembles appear to stay remarkably close to the assumed probability distribution functions. Copyright © 2010 Royal Meteorological Society [source]

    4D-Var assimilation of MERIS total column water-vapour retrievals over land

    Peter Bauer
    Abstract Experiments with the active assimilation of total column water-vapour retrievals from Envisat MERIS observations have been performed at the European Centre for Medium-Range Weather Forecasts (ECMWF), focusing on the summer 2006 African Monsoon Multidisciplinary Analysis (AMMA) field campaign period. A mechanism for data quality control, observation error definition and variational bias correction has been developed so that the data can be safely treated within 4D-Var, like other observations that are currently assimilated in the operational system. While data density is limited due to the restriction to daylight and cloud-free conditions, a systematic impact on mean moisture analysis was found, with distinct regional and seasonal features. The impact can last 1--2 days into the forecast but has little effect on forecast accuracy in terms of both moisture and dynamics. This is mainly explained by the weak dynamic activity in the areas of largest data impact. Analysis and short-range forecast evaluation with radiosonde observations revealed a strong dependence on radiosonde type. Compared with Vaisala RS92 observations, the addition of MERIS total column water-vapour observations produced neutral to positive impact, while contradictory results were obtained when all radiosonde types were used in generating the statistics. This highlights the issue of radiosonde moisture biases and the importance of sonde humidity bias correction in numerical weather prediction (NWP). Copyright © 2009 Royal Meteorological Society [source]

    The characteristics of Hessian singular vectors using an advanced data assimilation scheme

    A. R. Lawrence
    Abstract Initial condition uncertainty is a significant source of forecast error in numerical weather prediction. Singular vectors of the tangent linear propagator can identify directions in phase-space where initial errors are likely to make the largest contribution to forecast-error variance. The physical characteristics of these singular vectors depend on the choice of initial-time metric used to represent analysis-error covariances: the total-energy norm serves as a proxy to the analysis-error covariance matrix, whereas the Hessian of the cost function of a 4D-Var assimilation scheme represents a more sophisticated estimate of the analysis-error covariances, consistent with observation and background-error covariances used in the 4D-Var scheme. This study examines and compares the structure of singular vectors computed with the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System using these two types of initial metrics. Unlike earlier studies that use background errors derived from lagged forecast differences (the NMC method), the background-error covariance matrix in the Hessian metric is based on statistics from an ensemble of 4D-Vars using perturbed observations, which produces tighter correlations of background-error statistics than in previous formulations. In light of these new background-error statistics, this article re-examines the properties of Hessian singular vectors (and their relationship to total-energy singular vectors) using cases from different periods between 2003 and 2005. Energy profiles and wavenumber spectra reveal that the total-energy singular vectors are similar to Hessian singular vectors that use all observation types in the operational 4D-Var assimilation. This is in contrast to the structure of Hessian singular vectors without observations. Increasing the observation density tends to reduce the spatial scale of the Hessian singular vectors. Copyright © 2009 Royal Meteorological Society [source]

    The potential of variational retrieval of temperature and humidity profiles from Meteosat Second Generation observations

    F. Di Giuseppe
    Abstract The quality of temperature and humidity retrievals from the infrared Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensors on the geostationary Meteosat Second Generation (MSG) satellites is assessed by means of a one-dimensional variational algorithm. The study is performed with the aim of improving the spatial and temporal resolution of available observations to feed analysis systems designed for high-resolution regional-scale numerical weather prediction (NWP) models. The non-hydrostatic forecast model COSMO in the ARPA-SIMC operational configuration is used to provide background fields. Only clear-sky observations over sea are processed. An optimized one-dimensional variational set-up comprised of two water-vapour and three window channels is selected. It maximizes the reduction of errors in the model backgrounds while ensuring ease of operational implementation through accurate bias correction procedures and correct radiative transfer simulations. The 1Dvar retrieval quality is first quantified in relative terms, employing statistics to estimate the reduction in the background model errors. Additionally the absolute retrieval accuracy is assessed by comparing the analysis with independent radiosonde observations. The inclusion of satellite data brings a substantial reduction in the warm and dry biases present in the forecast model. Moreover it is shown that the use of the retrieved profiles generated by the 1Dvar in the COSMO nudging scheme can locally reduce forecast errors. Copyright © 2009 Royal Meteorological Society [source]

    The optimal density of atmospheric sounder observations in the Met Office NWP system

    M. L. Dando
    Abstract Large numbers of satellite observations are discarded from the numerical weather prediction (NWP) process because high-density observations may have a negative impact on the analysis. In current assimilation schemes, the observation error covariance matrix R is usually represented as a diagonal matrix, which assumes there are no correlations in the observation errors and that each observation is an independent piece of information. This is not the case when there are strong error correlations and this can lead to a degraded analysis. The experiments conducted in this study were designed to identify the optimal density and to determine if there were circumstances when exceeding this density might be beneficial to forecast skill. The global optimal separation distance of Advanced TIROS Operational Vertical Sounder (ATOVS) observations was identified by comparing global forecast errors produced using different densities of ATOVS. The global average of the absolute forecast error produced by each different density was found for a 3-week period from December 2004 to January 2005. The results showed that, when using the Met Office NWP system with a horizontal model resolution of ,60 km, the lowest global forecast errors were produced when using separation distances of 115,154 km. However, localized regions of the atmosphere containing large gradients such as frontal regions may benefit from thinning distances as small as 40 km and therefore the global optimal separation distance is not necessarily applicable in these circumstances. Copyright © 2007 Royal Meteorological Society [source]

    Variable cirrus shading during CSIP IOP 5.

    I: Effects on the initiation of convection
    Abstract Observations from the Convective Storm Initiation Project (CSIP) show that on 29 June 2005 (Intensive Observation Period 5) cirrus patches left over from previous thunderstorms reduced surface sensible and latent heat fluxes in the CSIP area. Large-eddy model (LEM) simulations, using moving positive surface-flux anomalies, show that we expect the observed moving gaps in the cirrus cover to significantly aid convective initiation. In these simulations, the timing of the CI is largely determined by the amount of heat added to the boundary layer, but weak convergence at the rear edge of the moving anomalies is also significant. Meteosat and rain-radar data are combined to determine the position of convective initiation for all 25 trackable showers in the CSIP area. The results are consistent with the LEM simulations, with showers initiating at the rear edge of gaps, at the leading edge of the anvil, or in clear skies, in all but one of the cases. The initiation occurs in relatively clear skies in all but two of the cases, with the exceptions probably linked to orographic effects. For numerical weather prediction, the case highlights the importance of predicting and assimilating cloud cover. The results show that in the absence of stronger forcings, weak forcings, such as from the observed cirrus shading, can determine the precise location and timing of convective initiation. In such cases, since the effects of shading by cirrus anvils from previous convective storms are relatively unpredictable, this is expected to limit the predictability of the convective initiation. Copyright © 2007 Royal Meteorological Society [source]

    Modelling suppressed and active convection.

    Comparing a numerical weather prediction, cloud-resolving, single-column model
    Abstract This paper describes the design of and basic results from a case study to compare simulations of convection over the Tropical West Pacific. Simulations are carried out using a cloud-resolving model (CRM), a global numerical weather prediction (NWP) model and a single-column version of the NWP model (SCM). The experimental design for each model type is discussed and then results are compared. The periods simulated each include a regime with strong convective activity, a much more suppressed regime with far less convection, as well as the transition between these regimes. The description of the design and basic results from this study are given in some detail, as a study including all these model types is relatively new. Comparing the local forcing due to the dynamics in the NWP model with the observed forcing used to drive the CRM and SCM it is found that there is good agreement for one period chosen but significant differences for another. This is also seen in fields such as rain rate and top-of-atmosphere radiation. Using the period with good agreement we are able to identify examples of biases in the NWP model that are also reproduced in the SCM. Also discussed are examples of biases in the NWP simulation that are not reproduced in the SCM. It is suggested that understanding which biases in the SCM are consistent with the full NWP model can help focus the use of an SCM in this framework. © Crown Copyright 2007. Reproduced with the permission of the Controller of HMSO. Published by John Wiley & Sons, Ltd [source]

    Effects of data selection and error specification on the assimilation of AIRS data,

    J. Joiner
    Abstract The Atmospheric InfraRed Sounder (AIRS), flying aboard NASA's Aqua satellite with the Advanced Microwave Sounding Unit-A (AMSU-A) and four other instruments, has been providing data for use in numerical weather prediction and data assimilation systems for over three years. The full AIRS data set is currently not transmitted in near-real-time to the prediction/assimilation centres. Instead, data sets with reduced spatial and spectral information are produced and made available within three hours of the observation time. In this paper, we evaluate the use of different channel selections and error specifications. We achieve significant positive impact from the Aqua AIRS/AMSU-A combination during our experimental time period of January 2003. The best results are obtained using a set of 156 channels that do not include any in the H2O band between 1080 and 2100 cm,1. The H2O band channels have a large influence on both temperature and humidity analyses. If observation and background errors are not properly specified, the partitioning of temperature and humidity information from these channels will not be correct, and this can lead to a degradation in forecast skill. Therefore, we suggest that it is important to focus on background error specification in order to maximize the impact from AIRS and similar instruments. In addition, we find that changing the specified channel errors has a significant effect on the amount of data that enters the analysis as a result of quality control thresholds that are related to the errors. However, moderate changes to the channel errors do not significantly impact forecast skill with the 156 channel set. We also examine the effects of different types of spatial data reduction on assimilated data sets and NWP forecast skill. Whether we pick the centre or the warmest AIRS pixel in a 3 × 3 array affects the amount of data ingested by the analysis but does not have a statistically significant impact on the forecast skill. Copyright © Published in 2007 by John Wiley & Sons, Ltd. [source]

    Fibonacci grids: A novel approach to global modelling

    Richard Swinbank
    Abstract Recent years have seen a resurgence of interest in a variety of non-standard computational grids for global numerical prediction. The motivation has been to reduce problems associated with the converging meridians and the polar singularities of conventional regular latitude,longitude grids. A further impetus has come from the adoption of massively parallel computers, for which it is necessary to distribute work equitably across the processors; this is more practicable for some non-standard grids. Desirable attributes of a grid for high-order spatial finite differencing are: (i) geometrical regularity; (ii) a homogeneous and approximately isotropic spatial resolution; (iii) a low proportion of the grid points where the numerical procedures require special customization (such as near coordinate singularities or grid edges); (iv) ease of parallelization. One family of grid arrangements which, to our knowledge, has never before been applied to numerical weather prediction, but which appears to offer several technical advantages, are what we shall refer to as ,Fibonacci grids'. These grids possess virtually uniform and isotropic resolution, with an equal area for each grid point. There are only two compact singular regions on a sphere that require customized numerics. We demonstrate the practicality of this type of grid in shallow-water simulations, and discuss the prospects for efficiently using these frameworks in three-dimensional weather prediction or climate models. © Crown copyright, 2006. Royal Meteorological Society [source]

    Issues in targeted observing

    (Invited paper for the Q. J. R. Meteorol.
    Abstract This paper summarizes successes and limitations of targeted observing field programmes starting from the Fronts and Atlantic Storm-Track Experiment in 1997 through recent programmes targeting winter storms and tropical cyclones. These field programmes have produced average reductions in short-range forecast errors of about 10 per cent over regional verification areas, and maximum forecast error reductions as large as 50 per cent in certain cases. The majority of targeting cases investigated so far involve sets of dropsondes and other observation data that provide partial coverage of target areas. The primary scientific challenges for targeting include the refinement of objective methods that can identify optimal times and locations for targeted observations, as well as identify the specific types of satellite and in situ measurements that are required for the improvement of numerical weather forecasts. The most advanced targeting procedures, at present, include: the ensemble transform Kalman Filter, Hessian singular vectors, and observation-space targeting using the adjoint of a variational data assimilation procedure. Targeted observing remains an active research topic in numerical weather prediction, with plans for continued refinement of objective targeting procedures, and field tests of new satellite and in situ observing systems. Copyright © 2005 Royal Meteorological Society [source]

    Adaptive thinning of atmospheric observations in data assimilation with vector quantization and filtering methods

    T. Ochotta
    Abstract In data assimilation for numerical weather prediction, measurements of various observation systems are combined with background data to define initial states for the forecasts. Current and future observation systems, in particular satellite instruments, produce large numbers of measurements with high spatial and temporal density. Such datasets significantly increase the computational costs of the assimilation and, moreover, can violate the assumption of spatially independent observation errors. To ameliorate these problems, we propose two greedy thinning algorithms, which reduce the number of assimilated observations while retaining the essential information content of the data. In the first method, the number of points in the output set is increased iteratively. We use a clustering method with a distance metric that combines spatial distance with difference in observation values. In a second scheme, we iteratively estimate the redundancy of the current observation set and remove the most redundant data points. We evaluate the proposed methods with respect to a geometric error measure and compare them with a uniform sampling scheme. We obtain good representations of the original data with thinnings retaining only a small portion of observations. We also evaluate our thinnings of ATOVS satellite data using the assimilation system of the Deutscher Wetterdienst. Impact of the thinning on the analysed fields and on the subsequent forecasts is discussed. Copyright © 2005 Royal Meteorological Society [source]

    Use of the MODIS imager to help deal with AIRS cloudy radiances

    Mohamed Dahoui
    Abstract The assimilation of the Atmospheric InfraRed Sounder (AIRS) data is expected to improve the quality of NWP products. Currently, operational use of such data is limited to the cloud-free pixels or to the channels far above the clouds for cloudy pixels. This paper focuses on the validation of various cloud-detection schemes applied to AIRS data. The clouds are detected and characterized, in cloud-top and cover, by using the NESDIS, ECMWF, CO2 -slicing and MLEV schemes. These four different AIRS cloud descriptions are evaluated by independent information retrieved with the Météo-France cloud mask applied to MODIS data and taken as our reference. The validation for a ten-day period over the North-east Atlantic is presented. The use of satellite cloudy radiances is a great challenge for numerical weather prediction. Work is in progress to assimilate such data by using enhanced observation operators dealing with clouds. In this work, we try to contribute to this effort by investigating the linearity assumption of an observation operator, with a simple diagnostic cloud scheme, for different cloud types. Copyright © 2005 Royal Meteorological Society [source]