Data for 2002-2011 is simulated in an experiment from the United States Agency for International Development (USAID) Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS). The community Noah land surface model v3.2 (Chen et al., 1996; Ek et al., 2003) is forced in uncoupled mode with NOAA-CPC African Rainfall Estimation Algorithm 2.0 (RFE) rainfall (Xie and Arkin, 1997) and
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National Center for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS) meteorological data (Derber et al., 1991). This configuration was designed to simulate surface energy budget variables that complement the RFE rainfall record. RFE is a near-real time product that was developed for operational monitoring of growing conditions in Africa. FEWS NET uses 10-day accumulated RFE for drought monitoring activities. RFE is a blend of cloud top temperature from Meteosat 7 geostationary satellite infrared data, microwave data from Special Sensor Microwave/Imager (SSM/I) and Advanced Microwave Sounding Unit (AMSU), and WMO Global Telecommunication System (GTS) station data for satellite bias removal. These sources are merged for a daily rainfall estimate at 0.1° resolution. For sub-daily forcing of the land surface model RFE 2.0 was broken into 6- hourly estimates. Disaggregation was accomplished using a temporal weighting scheme that distributes RFE 2.0 daily total rainfall into sub-daily rainfall totals that are consistent with the temporal distribution of rainfall in the GDAS-CMAP product (Gottschalck et al., 2005). GDAS meteorological data come from surface, atmospheric, and satellite observations that are gridded for use in weather forecasting models such as the NCEP Global Forecast System.
Surface temperature was not used because it was not available in the FLDAS dataset as a monthly-averaged variable. Because monthly emissivity did not change between years in the simulation, interannual LWup variability would be similar to that of surface temperature. This was confirmed by comparing Noah land surface model-simulated surface temperature and calculated LWup from a similar dataset, the 0.25 degree monthly Global Land Data Assimilation System Noah simulation from NASA (GLDAS_NOAH025_M) (Rodell et al., 2004). LWup was calculated with the same method used in this analysis (detailed in Section
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3.1). Figure 2 shows that upwelling longwave radiation and surface temperature are highly correlated in the GLDAS Noah dataset (R= 0.80-1). Upwelling longwave radiation
anomalies are synonymous with anomalous surface temperature. An advantage to analysis with LWup is that it enables the impact of surface energy balance terms on surface heating and cooling to be examined based upon the magnitude of their anomalies (all are in units W m-2).
The analysis domain for this paper is a region in East Africa from 5 °N – 5°S, 33 °E – 43 °E which encompasses Kenya and parts of Tanzania, Uganda, South Sudan, Ethiopia, and Somalia. We use surface energy budget terms and some atmospheric forcing variables that are output from the model as 24-hr averages. In this paper we analyze monthly mean data for April, the center month of the Kenyan ‘Long Rains’ season, for the period 2002-2011. Forcing data and surface parameters used for the FLDAS Noah simulation are listed in Table 1.
The East Africa region is shown in Figure 3. Figure 3a shows a regional map of April climatological daily rainfall, averaged over 2002-2011 with RFE 2.0 (Xie and Arkin, 1997). Figure 3b shows green vegetation fraction (as a percent) from NESDIS/NOAA (Gutman and Ignatov, 1998). Green vegetation fraction is the percent of each grid cell where midday downward insolation is intercepted by a photosynthetically active green canopy (Chen et al., 1996). It is an important land surface parameter used by the Noah model to calculate total evapotranspiration for each grid cell. As these maps show, rainfall and vegetation density are highly variable across the region. April rainfall is highest near Mount Kenya (central Kenya), Mount Meru and Kilimanjaro (north Tanzania) and near Lake Victoria. April rainfall is lowest in the zone from East Kenya to southeast Somalia and in very arid
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northeast Kenya in the Chalbi Desert near Lake Turkana. Southern Ethiopia and areas in the region’s southwest, including parts of Uganda, Kenya, and Tanzania, receive moderate rainfall in April. As seen in Figure 3b, vegetation density varies dramatically across the region, from near zero to 85% cover. Vegetation density is highest in the southwest and in coast Kenya and southern Ethiopia. The April pattern is similar to what an annual average would show because wet areas in April also receive rain through much of the year. The exception is in southern Ethiopia and the semi-arid east Kenya-southwest Somalia zone, where rain comes in two distinct and short seasons. In these areas April is one of the most productive months in terms of vegetation photosynthetic activity.
In this analysis only meteorological data are used to force the model, so annual differences in monthly mean upwelling longwave radiation are due to variations in weather and its impact to soil moisture. In the FLDAS experiment biological and reflective properties of the land surface do not change between years. Green vegetation fraction data (Gutman and Ignatov, 1998) and surface albedo data (Csiszar and Gutman, 1999) are NESDIS/NOAA 5- year monthly climatology grids, i.e. they vary in space and by month but not between years, that are derived from NOAA Advanced Very High Resolution Radiometer (AVHRR) satellite data. These datasets were designed for use in numerical weather prediction models. We used these climatological datasets because NASA’s Land Information System, the platform used to run Noah and other models, is set up to ingest them. Using a monthly climatology instead of measured greenness and albedo introduces artificiality to the modeled surface energy budget and is a limitation of this analysis. Use of measured greenness data has been shown to improve partitioning between surface heating and evapotranspiration, which impacts the surface energy budget, planetary boundary layer evolution, cloud, and
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convection. Advancements in land modeling are geared towards assimilating remotely sensed data and incorporating dynamic vegetation, where vegetation growth is modeled not prescribed, into models.
Table 1. Selected FLDAS Noah simulation variables
Variable Variable type Source
Mean rain rate Forcing data RFE 2.0 (Xie and Arkin, 1997) Other
meteorological variables
Forcing data National Center for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS) (Derber et al., 1991)
Elevation Static parameter GTOPO30 Global 30 Arc Second Elevation Dataset (Gesch et al., 1999)
Soil type Static parameter FAO Soils Database (Reynolds et al., 2000)
Vegetation type Static parameter University of Maryland 1 km vegetation classification (Hansen et al., 2000)
Green vegetation fraction
Monthly climatology
NESDIS/NOAA 0.144 degree monthly 5-year climatology green vegetation fraction from NCEP (Gutman and Ignatov, 1998)
Surface albedo Monthly climatology
NESDIS/NOAA 0.144 degree monthly 5-year climatology surface albedo from NCEP (Csiszar and Gutman, 1999)