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. Similarly, for model evaluations, multiple nonmodel products along with their uncertainties should be used. Acknowledgments This work was supported by NSF (EF-1238908 and AGS-0944101) and NASA (NNX14AM02G). Two anonymous reviewers are thanked for helpful comments that significantly increased the clarity of our presentation. The USDA-ARS Southwest Watershed Research Center is thanked for providing long-term, high-quality precipitation and soil moisture data used in this study (available at http
. Similarly, for model evaluations, multiple nonmodel products along with their uncertainties should be used. Acknowledgments This work was supported by NSF (EF-1238908 and AGS-0944101) and NASA (NNX14AM02G). Two anonymous reviewers are thanked for helpful comments that significantly increased the clarity of our presentation. The USDA-ARS Southwest Watershed Research Center is thanked for providing long-term, high-quality precipitation and soil moisture data used in this study (available at http
observational rainfall data are often lacking in Africa (e.g., Nicholson et al. 2003 ). The coverage of rain gauges is low (~1 for every 5000 km 2 ) and even shrinking in recent decades; data quality is sometimes questionable because of outdated instrumentation and manual readings. Frequent coding errors lead to erroneous extreme daily rainfall amounts, which do not get eliminated by simple quality controls in widely used datasets such as the NOAA Integrated Surface Database ( https
observational rainfall data are often lacking in Africa (e.g., Nicholson et al. 2003 ). The coverage of rain gauges is low (~1 for every 5000 km 2 ) and even shrinking in recent decades; data quality is sometimes questionable because of outdated instrumentation and manual readings. Frequent coding errors lead to erroneous extreme daily rainfall amounts, which do not get eliminated by simple quality controls in widely used datasets such as the NOAA Integrated Surface Database ( https
. Finally, FGOALS-s2 does not show any clouds at all below 600 hPa. This is likely a data error, but at least it is physically consistent with very low RH (see section 4b ). As discussed in the introduction and section 3 , low-level wind is important for the stratus decks, as it controls advection of T and q , and contributes to nighttime turbulent mixing. In ERA-I an LLJ is evident during the night, reaching 6 m s −1 at 925 hPa at 0600 UTC ( Figs. 3e,f ). This value is realistic, as shown from
. Finally, FGOALS-s2 does not show any clouds at all below 600 hPa. This is likely a data error, but at least it is physically consistent with very low RH (see section 4b ). As discussed in the introduction and section 3 , low-level wind is important for the stratus decks, as it controls advection of T and q , and contributes to nighttime turbulent mixing. In ERA-I an LLJ is evident during the night, reaching 6 m s −1 at 925 hPa at 0600 UTC ( Figs. 3e,f ). This value is realistic, as shown from
1. Introduction Warm season rainfall in the tropics varies greatly on wide-ranging time scales and remains a challenging issue for weather and climate prediction. Here we focus on the diurnal cycle of West African rainfall. A physical understanding of how the diurnal cycle is controlled is crucial for simulating and predicting changes in both mean precipitation and extreme rainfall events. For example, to confidently project how rainfall will change in the future under global warming, climate
1. Introduction Warm season rainfall in the tropics varies greatly on wide-ranging time scales and remains a challenging issue for weather and climate prediction. Here we focus on the diurnal cycle of West African rainfall. A physical understanding of how the diurnal cycle is controlled is crucial for simulating and predicting changes in both mean precipitation and extreme rainfall events. For example, to confidently project how rainfall will change in the future under global warming, climate
the reliability of land surface water (especially) and energy budgets. The NCEP daily gridded precipitation dataset has a similarly fine (0.25° latitude–longitude) spatial resolution that is produced by an optimal interpolation (OI) ( Xie et al. 2007 ) objective analysis of totals from ~8000 rain gauge stations nationwide. Those data emanate from multiple independent observing systems and then are subject to several layers of quality control ( http
the reliability of land surface water (especially) and energy budgets. The NCEP daily gridded precipitation dataset has a similarly fine (0.25° latitude–longitude) spatial resolution that is produced by an optimal interpolation (OI) ( Xie et al. 2007 ) objective analysis of totals from ~8000 rain gauge stations nationwide. Those data emanate from multiple independent observing systems and then are subject to several layers of quality control ( http
explains the increased easterly wind flow that results from the LWM being centered on average at 16.2°N, 12.3°W. 3. Data and methodology a. Data The daily and monthly rainfall records covering 1979–2008 over the WAM region are used for 180 stations ( Fig. 1 ) in this study. The station rainfall totals were provided by the national weather services of countries across the region. The data were quality controlled, with values beyond physically reasonable limits being excluded. The majority of the station
explains the increased easterly wind flow that results from the LWM being centered on average at 16.2°N, 12.3°W. 3. Data and methodology a. Data The daily and monthly rainfall records covering 1979–2008 over the WAM region are used for 180 stations ( Fig. 1 ) in this study. The station rainfall totals were provided by the national weather services of countries across the region. The data were quality controlled, with values beyond physically reasonable limits being excluded. The majority of the station
resolutions have similar issues with the diurnal cycle as the GCMs, and altering cumulus parameterization schemes does not help to capture the nocturnal rainfall peaks. This inability suggests that the errors are at least partially associated with the parameterized physical processes that control rainfall production in the models. One approach to avoiding the use of cumulus parameterization in models is to conduct convection-permitting simulations using spatial resolutions on the order of 4 km or finer
resolutions have similar issues with the diurnal cycle as the GCMs, and altering cumulus parameterization schemes does not help to capture the nocturnal rainfall peaks. This inability suggests that the errors are at least partially associated with the parameterized physical processes that control rainfall production in the models. One approach to avoiding the use of cumulus parameterization in models is to conduct convection-permitting simulations using spatial resolutions on the order of 4 km or finer
