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Abstract
Precipitation during the boreal winter dry season in tropical West Africa is rare but occasionally results in significant impacts on the local population. The dynamics and predictability of this phenomenon have been studied very little. Here, a statistical evaluation of the climatology, dynamics, and predictions of dry-season wet events is presented for the region 7.5°–15°N, 10°W–10°E. The analysis is based upon Global Precipitation Climatology Project (GPCP) merged satellite–gauge pentad rainfall estimates and 5-day 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) precipitation forecasts, and covers the 23 dry seasons (November–February) during 1979/80–2001/02. Wet events are defined as pentads with an area-averaged precipitation anomaly of more than +200% with respect to the mean seasonal cycle. Composites of the 43 identified events indicate an association with a trough over northwestern Africa, a tropical plume on its eastern side, unusual precipitation at the northern and western fringes of the Sahara, and reduced surface pressure over the Sahara, which allows an inflow of moist southerlies from the Gulf of Guinea to feed the unusual dry-season rainfalls. The results give evidence for a preconditioning by another disturbance about 1 week prior to the precipitation event. The ERA-40 forecasts show a high temporal correlation with observations, a general wet bias, but a somewhat too low number of wet events. With 53% of all identified events correctly forecasted and only 32% of forecasted events not verified, the model shows moderate skill in contrast to the prediction of many other tropical precipitation systems. A separate consideration of hits, misses, and false alarms corroborates the previously proposed hypothesis that a strong extratropical influence enhances the quality of predictions in this region. The results should encourage weather services in West Africa to take advantage of available dry-season precipitation forecasts in terms of the dissemination of early warnings.
Abstract
Precipitation during the boreal winter dry season in tropical West Africa is rare but occasionally results in significant impacts on the local population. The dynamics and predictability of this phenomenon have been studied very little. Here, a statistical evaluation of the climatology, dynamics, and predictions of dry-season wet events is presented for the region 7.5°–15°N, 10°W–10°E. The analysis is based upon Global Precipitation Climatology Project (GPCP) merged satellite–gauge pentad rainfall estimates and 5-day 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) precipitation forecasts, and covers the 23 dry seasons (November–February) during 1979/80–2001/02. Wet events are defined as pentads with an area-averaged precipitation anomaly of more than +200% with respect to the mean seasonal cycle. Composites of the 43 identified events indicate an association with a trough over northwestern Africa, a tropical plume on its eastern side, unusual precipitation at the northern and western fringes of the Sahara, and reduced surface pressure over the Sahara, which allows an inflow of moist southerlies from the Gulf of Guinea to feed the unusual dry-season rainfalls. The results give evidence for a preconditioning by another disturbance about 1 week prior to the precipitation event. The ERA-40 forecasts show a high temporal correlation with observations, a general wet bias, but a somewhat too low number of wet events. With 53% of all identified events correctly forecasted and only 32% of forecasted events not verified, the model shows moderate skill in contrast to the prediction of many other tropical precipitation systems. A separate consideration of hits, misses, and false alarms corroborates the previously proposed hypothesis that a strong extratropical influence enhances the quality of predictions in this region. The results should encourage weather services in West Africa to take advantage of available dry-season precipitation forecasts in terms of the dissemination of early warnings.
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.
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.
Abstract
The variability of the West African monsoon on the intraseasonal time scale is a major issue for agricultural strategy, as the occurrence of dry spells can strongly impact yields of rain-fed crops. This study investigates this intraseasonal variability of rainfall over West Africa and gives a first overview of its predictability at a medium lead time.
A statistical method, the singular spectrum analysis, is applied to a ground-based rainfall index in West Africa to describe first temporal patterns of the main leading modes of intraseasonal variability. The results point out the existence of one oscillatory mode of 34 days, one of 20 days, and one of 14 days. The same methodology is applied to rainfall from two reanalysis datasets and to deep convection from satellite data in order to assess the accuracy of the representation of intraseasonal variability in these datasets. It is shown that although the day-to-day variability of rainfall is not well captured in these datasets, intraseasonal features and, in particular, the low-frequency mode are very well reproduced.
The medium lead-time predictability (5–10 days) of the intraseasonal modes is investigated using both the dynamical forecast scheme of the ECMWF and a statistical method, the maximum entropy method. For the latter method, an operational application using unfiltered input data is also considered. The performance of these prediction schemes is compared using a simple reference technique in which forecasts are based entirely on persistence. It is found that statistical predictions are much more promising than the dynamical ones, though they encounter problems when applied operationally. In an operational application, the forecast skill for the 10–90-day intraseasonal band is low but the predictability of individual intraseasonal modes is higher. The stability of the forecast skill levels is influenced by the characteristics of the intraseasonal mode. When the characteristics (i.e., amplitude and period) of the considered intraseasonal mode are well defined, skillful forecasts can be obtained. However, when the characteristics change rapidly, the forecast fails.
Abstract
The variability of the West African monsoon on the intraseasonal time scale is a major issue for agricultural strategy, as the occurrence of dry spells can strongly impact yields of rain-fed crops. This study investigates this intraseasonal variability of rainfall over West Africa and gives a first overview of its predictability at a medium lead time.
A statistical method, the singular spectrum analysis, is applied to a ground-based rainfall index in West Africa to describe first temporal patterns of the main leading modes of intraseasonal variability. The results point out the existence of one oscillatory mode of 34 days, one of 20 days, and one of 14 days. The same methodology is applied to rainfall from two reanalysis datasets and to deep convection from satellite data in order to assess the accuracy of the representation of intraseasonal variability in these datasets. It is shown that although the day-to-day variability of rainfall is not well captured in these datasets, intraseasonal features and, in particular, the low-frequency mode are very well reproduced.
The medium lead-time predictability (5–10 days) of the intraseasonal modes is investigated using both the dynamical forecast scheme of the ECMWF and a statistical method, the maximum entropy method. For the latter method, an operational application using unfiltered input data is also considered. The performance of these prediction schemes is compared using a simple reference technique in which forecasts are based entirely on persistence. It is found that statistical predictions are much more promising than the dynamical ones, though they encounter problems when applied operationally. In an operational application, the forecast skill for the 10–90-day intraseasonal band is low but the predictability of individual intraseasonal modes is higher. The stability of the forecast skill levels is influenced by the characteristics of the intraseasonal mode. When the characteristics (i.e., amplitude and period) of the considered intraseasonal mode are well defined, skillful forecasts can be obtained. However, when the characteristics change rapidly, the forecast fails.
