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O. Bock and M. Nuret

Abstract

This paper assesses the performance of the European Centre for Medium-Range Weather Forecasts-Integrated Forecast System (ECMWF-IFS) operational analysis and NCEP–NCAR reanalyses I and II over West Africa, using precipitable water vapor (PWV) retrievals from a network of ground-based GPS receivers operated during the African Monsoon Multidisciplinary Analysis (AMMA). The model analyses show reasonable agreement with GPS PWV from 5-daily to monthly means. Errors increase at shorter time scales, indicating that these global NWP models have difficulty in handling the diurnal cycle and moist processes at the synoptic scale. The ECMWF-IFS analysis shows better agreement with GPS PWV than do the NCEP–NCAR reanalyses (the RMS error is smaller by a factor of 2). The model changes in ECMWF-IFS were not clearly reflected in the PWV error over the period of study (2005–08). Radiosonde humidity biases are diagnosed compared to GPS PWV. The impacts of these biases are evidenced in all three model analyses at the level of the diurnal cycle. The results point to a dry bias in the ECMWF analysis in 2006 when Vaisala RS80-A soundings were assimilated, and a diurnally varying bias when Vaisala RS92 or Modem M2K2 soundings were assimilated: dry during day and wet during night. The overall bias is offset to wetter values in NCEP–NCAR reanalysis II, but the diurnal variation of the bias is observed too. Radiosonde bias correction is necessary to reduce NWP model analysis humidity biases and improve precipitation forecast skill. The study points to a wet bias in the Vaisala RS92 data at nighttime and suggests that caution be used when establishing a bias correction scheme.

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C. Faccani, F. Rabier, N. Fourrié, A. Agusti-Panareda, F. Karbou, P. Moll, J.-P. Lafore, M. Nuret, F. Hdidou, and O. Bock

Abstract

The high vertical density soundings recorded during the 2006 African Monsoon Multidisciplinary Analysis (AMMA) campaign are assimilated into the French numerical weather prediction Action de Recherche Petite Echelle Grande Echelle (ARPEGE) four-dimensional variational data assimilation (4DVAR) system, with and without a bias correction for relative humidity. Four different experiments are carried out to assess the impacts of the added observations. The analyses and forecasts from these different scenarios are evaluated over western Africa. For the full experiment using all data together with a bias correction, the humidity analysis is in better agreement with surface observations and independent GPS observations than it was for the other experiments. AMMA data also improve the African easterly jet (AEJ) on its southeasterly side, and when they are used with an appropriate bias correction, the daily and monthly averaged precipitation results are in relatively good agreement with the satellite-based precipitation estimates. Forecast scores are computed with respect to surface observations, radiosondes, and analyses from the European Centre for Medium-Range Weather Forecasts (ECMWF). The positive impacts of additional radiosonde observations (with a relevant bias correction) are found to propagate downstream with a positive impact over Europe at the 2–3-day forecast range.

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