The field experiment of the African Monsoon Multidisciplinary Analysis (AMMA) project during the 2006 wet monsoon season provided an unprecedented amount of radiosonde/dropsonde data over the West African region. This paper explores the usage and impacts of this invaluable dataset in the European Centre for Medium-Range Weather Forecasts analyses and forecasts. These soundings are the only source of data that can provide 3D information on the thermodynamic and dynamic structures of the lower troposphere over continental West Africa. They are particularly important for the Sahel region located between 128 and 208N, which is characterized by large gradients in temperature and moisture in the lower troposphere. An assimilation experiment comparison between the pre-AMMA and AMMA radiosonde networks shows that the extra AMMA soundings have a significant analysis impact on the low-level temperature over the Sahel and on the structure of the African easterly jet. However, the impacts of the extra AMMA data on the forecast disappear after 24 h. The soundings reveal large model biases in boundary layer temperature over the northern and eastern Sahel, which are consistent with the well-known model biases in cloud, rainfall, and radiation. Large analysis increments in temperature lead to increments in divergence and subsidence, which act to suppress convection. Thus, the analysis increments appear to have an undesirable feedback on the cloud and temperature model biases. The impact of the AMMA soundings on the African easterly jet is to enhance and extend the jet streak to 158E, that is, toward the eastern part of the Sahel. No observations are assimilated east of 158E at the level of the African easterly jet to support the jet enhancement farther east. Comparisons with independent atmospheric cloud motion vectors indicate that the African easterly jet in the analysis is too weak over this data-sparse region. This could have implications for the development of African easterly waves in the model forecast. Further experimentation by assimilating atmospheric motion vectors-currently not used-could address this problem.

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Atmospheric Motion Vectors (AMVs) calculated by six different institutions (Brazil Center for Weather Prediction and Climate Studies/CPTEC/INPE, European Organization for the Exploitation of Meteorological Satellites/EUMETSAT, Japan Meteorological Agency/JMA, Korea Meteorological Administration/KMA, Unites States National Oceanic and Atmospheric Administration/NOAA, and the Satellite Application Facility on Support to Nowcasting and Very short range forecasting/NWCSAF) with JMA’s Himawari-8 satellite data and other common input data are here compared. The comparison is based on two different AMV input datasets, calculated with two different image triplets for 21 July 2016, and the use of a prescribed and a specific configuration. The main results of the study are summarized as follows: (1) the differences in the AMV datasets depend very much on the ‘AMV height assignment’ used and much less on the use of a prescribed or specific configuration; (2) the use of the ‘Common Quality Indicator (CQI)’ has a quantified skill in filtering collocated AMVs for an improved statistical agreement between centers; (3) Among the six AMV operational algorithms verified by this AMV Intercomparison, JMA AMV algorithm has the best overall performance considering all validation metrics, mainly due to its new height assignment method: ‘Optimal estimation method considering the observed infrared radiances, the vertical profile of the Numerical Weather Prediction wind, and the estimated brightness temperature using a radiative transfer model’.

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[1] Reflected spectral radiance measured by the Multiangle Imaging SpectroRadiometer (MISR) on the Terra satellite has been analyzed to determine the fraction of global cloudiness that appears to be spatially homogeneous over regions of various sizes. We exclude scenes with reflectivities less than 0.2 and high latitudes to avoid snow and ice. About 1.4 ± 0.3%, or 1 in 70, of 8.8 km cloudy regions measured at 275 m have a range of reflectivities less than ±5% of the central reflectivity value of the region. This pass rate changes slightly with viewing angle, and is sensitive to the size of the test window, rising to 11% for 1.1 km regions. The pass rate rises to a value of 2.3 ± 0.5% for 8.8 km regions if the measurement resolution is degraded to 1100 m. For the purposes of this study ''global'' cloudiness is limited to mid-morning clouds.

Atmospheric Motion Vectors (AMVs) calculated by six different institutions (Brazil Center for Weather Prediction and Climate Studies/CPTEC/INPE, European Organization for the Exploitation of Meteorological Satellites/EUMETSAT, Japan Meteorological Agency/JMA, Korea Meteorological Administration/KMA, Unites States National Oceanic and Atmospheric Administration/NOAA and the Satellite Application Facility on Support to Nowcasting/NWCSAF) with JMA’s Himawari-8 satellite data and other common input data are here compared. The comparison is based on two different AMV input datasets, calculated with two different image triplets for 21 July 2016, and the use of a prescribed and a specific configuration. The main results of the study are summarized as follows: (1) the differences in the AMV datasets depend very much on the “AMV height assignment” used and much less on the use of a prescribed or specific configuration; (2) the use of the “Common Quality Indicator (CQI)” has a quantified skill in filtering collocated AMVs for an improved statistical agreement between centers; (3) JMA AMV algorithm has the best overall performance considering all validation metrics, most likely due to its height assignment: “optimal estimation using observed radiance and NWP wind vertical profile”.

Observation system experiments (OSEs) are conducted to assess the potential impacts of horizontal line‐of‐sight wind profile observations from the Aeolus satellite on tropical cyclone (TC) forecasting. The OSEs utilize the operational Hurricane Weather and Research Forecasting (HWRF) model. The OSEs include 226 forecasts from seven TC cases in the Atlantic and Eastern Pacific basins. Comparisons between Aeolus and model background winds show that winds from Aeolus are consistently stronger than those from HWRF. Data assimilation statistics also demonstrate that the greatest potential impacts from the assimilation of Aeolus observations are likely to occur in the upper troposphere and lower stratosphere and within approximately 500 km from the TC centre. For TC forecasting applications, the assimilation of Aeolus observations improves TC intensity and size forecasts in the Eastern Pacific basin, while the results for track forecasts and results from the Atlantic basin are mixed. However, in both basins, the largest and most statistically significant, positive impacts from the assimilation of Aeolus observations occur when reconnaissance flight data are unavailable and during the early stages of TC development. The traditionally used forecast assessments of TC intensity, track and size are rooted in surface‐based metrics, and an additional investigation above the surface demonstrated larger improvements from assimilating Aeolus observations on TC wind structure above 400 hPa as compared to the lower troposphere. Several, different assessments throughout this study demonstrate higher uncertainty and the need for special consideration associated with assimilation techniques within 500 km from the TC centre.

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