Having access to the long-term historical daily weather data has been a roadblock for agricultural researchers who deal with production risk in data-sparse regions.
As an option, by loosely combining two existing global climate databases, HarvestChoice is synthesizing a 100-year daily weather dataset for Sub-Sahara Africa on 50-km grids. This post describes the methodology and provides access to the database, called SLATE (Synthesized Long-term Weather), formatted for input to the crop systems models.
100 Years of Daily Weather Data in SSA
Analyzing crop yield variabilities across large area (e.g., across Sub-Saharan Africa) requires long-term historical weather data for extensive coverage areas, but measurement datasets at the appropriate spatial and temporal scales (e.g., SSA-wide coverage with daily measurements for at least 30-year period) do not exist. Instead, there are following two commonly used climate/weather global data sources that are complementing each other, to some extent:
- University of East Anglia CRU-TS is a historic time-series climate database with monthly mean of six climate elements (cloud cover, diurnal temperature range, frost day frequency, precipitation, daily mean temperature, monthly average daily minimum and maximum temperatures, vapor pressure, and wet day frequency) over the global land area. Version 3.1 of the CRU-TS covers the time period from 1901 to 2009 at 0.5 degree spatial resolution. The database uses available station records and interpolation methods. See Mitchell and Jones (2005) for more details.
- NASA POWER, the Prediction of Worldwide Energy Resource, is a NASA database provides satellite-based estimates on surface meteorology and solar energy since 1997 at 1 degree spatial resolution. POWER provides all the climate elements that crop models typically requires, including solar radiation, daily temperature minimum and maximum, and rainfall.
Loosely combining the two databases, HarvestChoice synthesized a plausible historic daily weather database, SLATE (Synthesized Long-term Weather), that covers 100 year period from 1910 to 2009, for sub-Saharan Africa at 0.5 degree spatial resolution.
The synthesizing methodology is simple, and the process is around the finding of the closest match of total rainfall amount in a given month. For example, one POWER grid cell A (1 degree) covers four CRU-TS grid cell A1, A2, A3, and A4. From the CRU-TS grid cell A1, let's focus on a particular year and month - say July 1980, whose total rainfall amount was 100 mm. POWER does not have records of 1980. However, if we boldly assume the weather pattern of A is equality applicable to A1, A2, A3, and A4, POWER can provide 12 realizations (1997-2008) of July's daily weather pattern for the cell A. From the daily weather, one can quickly get monthly total rainfall of July in those years. If one of those years, say 2000, has the exactly same amount of total monthly rainfall of 100 mm (or the closest to 100 mm out of the 12 sets), then we pull the daily weather record, including all the other elements as well as rainfall, and plug into the July 1980 of the new database, SLATE.
This loose method is built upon a series of assumptions, thus this can not be regarded as a real data or replacement of measurement data. Synthesized daily rainfall patterns could be still far off from what really happened, even their monthly total match (or very close). However, we assume the outcome of this process is plausible enough to be used in a quick modeling exercise to examine the impact of rainfall-induced crop yield variability.
One advantage of this method over stochastic weather generators is the maintaining spatial correlation of rainfall. For example, due to their stochastic nature, weather generators often result in unlikely weather patterns at short distance (e.g., on a given season, drought and flood could occur in neighboring grid cells). By using CRU-TS as a reference, SLATE maintains the occurrences of regional climate events as they were recorded in the CRU-TS.
The SLATE v1.1 data files can be downloaded at:
https://hc.box.net/shared/2nr28vapjrb3dglpeydh (updated: 24 August 2011)
- Geographic coverage is sub-Sahara Africa.
- A GIS data layer of the cell boundary can be also downloaded for mapping purposes.
- File name indicates the ID of grid cell at 0.5 degree (30 arc-minute), described at HarvestChoice Grid Cell Databases (HCID).
- The weather data file format follows the standard weather file format used in DSSAT-CSM.
- Due to the limitation of the two-digit year, only 100 years has been processed from 1910 to 2009.
- Information in the header columns
- LAT and LONG columns indicate the centroid coordinates of the 0.5 degree grid cell from HCID.
- ELEV column represents mean elevation (m) within the cell boundary.
- TAV column is the average annual temperature (C), computed from the CRU-TS v3.1.
- AMP column was computed by the historical mean temperature of the warmest month minus the coldest month, computed from the CRU-TS v3.1.
- Weather elements
- SRAD: Solar radiation (mj/m2)
- TMAX: Daily maximum temperature (C)
- TMIN: Daily minimum temperature (C)
- RAIN: Daily total rainfall (mm)
- Selective validation with nearby weather station data
- Assessment of spatial structure of rainfall over time
The POWER agroclimatic datasets were obtained from the NASA Langley Research Center POWER Project funded through the NASA Earth Science Directorate Applied Science Program.