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Karim Houchi
,
Ad Stoffelen
,
Gert-Jan Marseille
, and
Jos De Kloe

Abstract

Quality control (QC) is among the most important steps in any data processing. These steps are elaborated for high-vertical-resolution radiosonde datasets that were gathered and analyzed to study atmospheric winds. The database is composed of different radiosonde wind-finding systems (WFSs), including radio theodolite, Loran C, and GPS. Inspection of this database, particularly for wind, wind shear, and ascent height increments (dz), showed a nonnegligible amount of outliers in radio theodolite data as compared to the two other WFSs, thus denoting quality differences between the various systems. An effective statistical QC (SQC) is then developed to isolate and eliminate outliers from the more realistic observations. Improving the accuracy of the radio theodolite WFS is critical to the derivation of the vertical motion and the vertical gradients of the horizontal wind—that is, wind shear—mainly because of the direct dependence of these quantities on dz. Based on the climatological distribution of the quality-controlled dz, a new approach is suggested to estimate these wind quantities for radio theodolite data. The approach is validated with the high-quality modern WFSs (Loran C and GPS). Although initially of reduced quality, applying SQC and using the climatological mean dz of 12-s smoothed radio theodolite profiles shows very good improvement in the climatological wind analyses of radio theodolite WFSs. Notably, the climatologies of ascent rate, vertical motion, horizontal wind, and vertical shear now look comparable for the various WFSs. Thus, the SQC processing steps prove essential and may be extended to other variables and measurement systems.

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Nedjeljka Žagar
,
Ad Stoffelen
,
Gert-Jan Marseille
,
Christophe Accadia
, and
Peter Schlüssel

Abstract

This paper deals with the dynamical aspect of variational data assimilation in the tropics and the role of the background-error covariances in the observing system simulation experiments for the tropics. The study uses a model that describes the horizontal structure of the potential temperature and wind fields in regions of deep tropical convection. The assimilation method is three- and four-dimensional variational data assimilation. The background-error covariance model for the assimilation is a multivariate model that includes the mass–wind couplings representative of equatorial inertio-gravity modes and equatorial Kelvin and mixed Rossby–gravity modes in addition to those representative of balanced equatorial Rossby waves. Spectra of the background errors based on these waves are derived from the tropical forecast errors of the European Centre for Medium-Range Weather Forecasts (ECMWF) model.

Tropical mass–wind (im)balances are illustrated by studying the potential impact of the spaceborne Doppler wind lidar (DWL) Atmospheric Dynamic Mission (ADM)-Aeolus, which measures horizontal line-of-sight (LOS) wind components. Several scenarios with two DWLs of ADM-Aeolus type are compared under different flow conditions and using different assumptions about the quality of the background-error covariances.

Results of three-dimensional variational data assimilation (3DVAR) illustrate the inefficiency of multivariate assimilation in the tropics. The consequence for the assimilation of LOS winds is that the missing part of the wind vector can hardly be reconstructed from the mass-field observations and applied balances as in the case of the midlatitudes.

Results of four-dimensional variational data assimilation (4DVAR) show that for large-scale tropical conditions and using reliable background-error statistics, differences among various DWL scenarios are not large. As the background-error covariances becomes less reliable, horizontal scales become smaller and the flow becomes less zonal, the importance of obtaining information about the wind vector increases. The added value of another DWL satellite increases as the quality of the background-error covariances deteriorates and it can be more than twice as large as in the case of reliable covariances.

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Ad Stoffelen
,
Gert-Jan Marseille
,
Erik Andersson
, and
David G. H. Tan
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Siebren de Haan
,
Gert-Jan Marseille
,
Paul de Valk
, and
John de Vries

Abstract

Denial experiments, also denoted observing system experiments (OSEs), are used to determine the impact of an observing system on the forecast quality of a numerical weather prediction (NWP) model. When the impact is neutral or positive, new observations from this observing system may be admitted to an operational forecasting system based on that NWP model. A drawback of the method applied in most denial experiments is that it neglects the operational time constraint on the delivery of observations. In a 10-week twin experiment with the operational High-Resolution Limited-Area Model (HIRLAM) at KNMI, the impact of additional ocean surface wind observations from the Advanced Scatterometer (ASCAT) on the forecast quality of the model has been verified under operational conditions. In the experiment, the operational model was used as reference, parallel to an augmented system in which the ASCAT winds were assimilated actively. Objective verification of the forecast with independent wind observations from moored buoys and ASCAT winds revealed a slight improvement in forecast skill as measured by a decrease in observation-minus-forecast standard deviation in the wind components for the short range (up to 24 h). A subjective analysis in a case study showed a realistic deepening of a low pressure system over the North Atlantic near the coast of Ireland through the assimilation of scatterometer data that were verified with radiosonde observations over Ireland. Based on these results, the decision was made to include ASCAT in operations at the next upgrade of the forecasting system.

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