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understanding of AEWs, but this is not always easily translated into forecast parameters such as rainfall, winds, or visibility. Figure 2 is a new consensus schematic of these various observable parameters and likely relationships. It was forged through many lengthy and animated conversations between researchers and forecasters, exemplifying the transfer of new insights into the dynamics of West African weather systems [e.g., a precipitable water perspective, its relationship with mesoscale convective
understanding of AEWs, but this is not always easily translated into forecast parameters such as rainfall, winds, or visibility. Figure 2 is a new consensus schematic of these various observable parameters and likely relationships. It was forged through many lengthy and animated conversations between researchers and forecasters, exemplifying the transfer of new insights into the dynamics of West African weather systems [e.g., a precipitable water perspective, its relationship with mesoscale convective
and the life cycle of mesoscale convective systems (MCSs). NASA Tropical Rainfall Measuring Mission (TRMM; Huffman et al. 2007 ) satellite observations have enabled a number of studies of the diurnal cycle over West Africa. Nesbitt and Zipser (2003) examined TRMM Precipitation Radar and Microwave Imager data for 1998–2001 and suggest that, over tropical land, rainfall associated with MCSs has a late evening peak while the intensity of MCSs is greatest in the late afternoon. The peak in non
and the life cycle of mesoscale convective systems (MCSs). NASA Tropical Rainfall Measuring Mission (TRMM; Huffman et al. 2007 ) satellite observations have enabled a number of studies of the diurnal cycle over West Africa. Nesbitt and Zipser (2003) examined TRMM Precipitation Radar and Microwave Imager data for 1998–2001 and suggest that, over tropical land, rainfall associated with MCSs has a late evening peak while the intensity of MCSs is greatest in the late afternoon. The peak in non
; Charney and Shukla 1981 ; Xue and Shukla 1993 ; Clark and Arritt 1995 ; Clark et al. 2001 ). The surface boundary focus of the present Ethiopian study is SST. However, El Niño–Southern Oscillation (ENSO)-related “predictability barrier” in Northern Hemisphere spring (e.g., Goswami and Shukla 1991 ; Webster and Yang 1992 ; Webster et al. 1998 ) can pose a major challenge to providing seasonal rainfall forecasts two or more months in advance in the tropics ( Goddard et al. 2001 ; Korecha and
; Charney and Shukla 1981 ; Xue and Shukla 1993 ; Clark and Arritt 1995 ; Clark et al. 2001 ). The surface boundary focus of the present Ethiopian study is SST. However, El Niño–Southern Oscillation (ENSO)-related “predictability barrier” in Northern Hemisphere spring (e.g., Goswami and Shukla 1991 ; Webster and Yang 1992 ; Webster et al. 1998 ) can pose a major challenge to providing seasonal rainfall forecasts two or more months in advance in the tropics ( Goddard et al. 2001 ; Korecha and
1. Introduction African easterly waves (AEWs) are synoptic-scale disturbances observed between the surface and midtroposphere from East (Sudan, Central African Republic, Chad) to West Africa (Niger, Nigeria, Burkina Faso, Mali) up to the Senegalese and Guinea coasts. They are the dominant synoptic weather systems in West Africa and the tropical Atlantic during boreal summer and modulate rainfall in West Africa in relation with the mesoscale convective systems (MCSs) ( Reed et al.1977 ; Fink
1. Introduction African easterly waves (AEWs) are synoptic-scale disturbances observed between the surface and midtroposphere from East (Sudan, Central African Republic, Chad) to West Africa (Niger, Nigeria, Burkina Faso, Mali) up to the Senegalese and Guinea coasts. They are the dominant synoptic weather systems in West Africa and the tropical Atlantic during boreal summer and modulate rainfall in West Africa in relation with the mesoscale convective systems (MCSs) ( Reed et al.1977 ; Fink
mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. XII: A diagnostic modeling study of precipitation development in narrow cloud-frontal rainbands . J. Atmos. Sci. , 20 , 2949 – 2972 , doi: 10.1175/1520-0469(1984)041<2949:TMAMSA>2.0.CO;2 . Saha , S. , and Coauthors , 2010 : The NCEP Climate Forecast System Reanalysis . Bull. Amer. Meteor. Soc. , 91 , 1015 – 1057 , doi: 10.1175/2010BAMS3001.1 . Sato , T. , H. Miura , M. Satoh , Y. N
mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. XII: A diagnostic modeling study of precipitation development in narrow cloud-frontal rainbands . J. Atmos. Sci. , 20 , 2949 – 2972 , doi: 10.1175/1520-0469(1984)041<2949:TMAMSA>2.0.CO;2 . Saha , S. , and Coauthors , 2010 : The NCEP Climate Forecast System Reanalysis . Bull. Amer. Meteor. Soc. , 91 , 1015 – 1057 , doi: 10.1175/2010BAMS3001.1 . Sato , T. , H. Miura , M. Satoh , Y. N
(1931–1993) and applications to long-range temperature forecasts . J. Climate , 9 , 1350 – 1362 . Illston, B. G. , Basara J. B. , Fisher D. K. , Elliott R. , Fiebrich C. A. , Crawford K. C. , Humes K. , and Hunt E. , 2008 : Mesoscale monitoring of soil moisture across a statewide network . J. Atmos. Oceanic Technol. , 25 , 167 – 182 . Koster, R. D. , and Coauthors , 2004 : Regions of strong coupling between soil moisture and precipitation . Science , 305 , 1138 – 1140
(1931–1993) and applications to long-range temperature forecasts . J. Climate , 9 , 1350 – 1362 . Illston, B. G. , Basara J. B. , Fisher D. K. , Elliott R. , Fiebrich C. A. , Crawford K. C. , Humes K. , and Hunt E. , 2008 : Mesoscale monitoring of soil moisture across a statewide network . J. Atmos. Oceanic Technol. , 25 , 167 – 182 . Koster, R. D. , and Coauthors , 2004 : Regions of strong coupling between soil moisture and precipitation . Science , 305 , 1138 – 1140
short-term forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF), this study revealed positive biases in LLJ speed, negative biases in low-level cloud cover, and therefore a large overestimation of solar radiation during the day. The LLJ biases, which should also affect advection and turbulent mixing, are consistent with biases in the north–south pressure gradient due to the misrepresentation of convection in the Sahel ( Marsham et al. 2013 ). For the more recent CMIP5
short-term forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF), this study revealed positive biases in LLJ speed, negative biases in low-level cloud cover, and therefore a large overestimation of solar radiation during the day. The LLJ biases, which should also affect advection and turbulent mixing, are consistent with biases in the north–south pressure gradient due to the misrepresentation of convection in the Sahel ( Marsham et al. 2013 ). For the more recent CMIP5
precipitation events is based on 6-hourly data of temperature, wind, and humidity from the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim, hereafter ERA-I; Dee et al. 2011 ). ERA-I covers the time period from 1979 to present with a horizontal resolution of 0.75° × 0.75° latitude–longitude and 60 vertical levels up to 0.1 hPa. The events will be assessed with regard to their anomalous total precipitable water (TPW) content with respect to the 1979–2014 period
precipitation events is based on 6-hourly data of temperature, wind, and humidity from the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim, hereafter ERA-I; Dee et al. 2011 ). ERA-I covers the time period from 1979 to present with a horizontal resolution of 0.75° × 0.75° latitude–longitude and 60 vertical levels up to 0.1 hPa. The events will be assessed with regard to their anomalous total precipitable water (TPW) content with respect to the 1979–2014 period
physical processes of the precipitation changes that are responsible for those reductions. 3. Model simulations and evaluation The National Oceanic and Atmospheric Administration (NOAA)/National Center for Atmospheric Research (NCAR) Weather Research and Forecasting (WRF; Skamarock et al. 2005 ) regional model, version 3.1.1, is used with 32 vertical levels, 90-km horizontal resolution, and a time step of 3 min. The top of the atmosphere is set at 20 hPa. Figure 2 shows the full model domain with
physical processes of the precipitation changes that are responsible for those reductions. 3. Model simulations and evaluation The National Oceanic and Atmospheric Administration (NOAA)/National Center for Atmospheric Research (NCAR) Weather Research and Forecasting (WRF; Skamarock et al. 2005 ) regional model, version 3.1.1, is used with 32 vertical levels, 90-km horizontal resolution, and a time step of 3 min. The top of the atmosphere is set at 20 hPa. Figure 2 shows the full model domain with
-Saharan drought did not appear in conjunction with unusually dry southerly surface air from the tropical Atlantic. Lamb (1983) also found that during the extremely dry year, the northward moisture flux across the Gulf of Guinea was shallow compared with the much deeper monsoon layer, during the less severe drought years. Cadet and Nnoli (1987) used one summer (1979) of the European Centre for Medium-Range Weather Forecasts (ECMWF) data to study the water vapor transport over Africa. They analyzed biweekly
-Saharan drought did not appear in conjunction with unusually dry southerly surface air from the tropical Atlantic. Lamb (1983) also found that during the extremely dry year, the northward moisture flux across the Gulf of Guinea was shallow compared with the much deeper monsoon layer, during the less severe drought years. Cadet and Nnoli (1987) used one summer (1979) of the European Centre for Medium-Range Weather Forecasts (ECMWF) data to study the water vapor transport over Africa. They analyzed biweekly
