cazzalli maritsah neomee
Jude Petkus
Going to Extremes and Surviving Extremes are television programmes made for Channel 4 by Nick Middleton. In each episode of the two series, Middleton visited an extreme area of the world to find out how people have adapted to life there.[1]
Both Going to Extremes and Surviving Extremes were accompanied by books of the same name, except in the USA where the latter was titled Extremes: Surviving the World's Harshest Environments.
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Figure 1a shows the overall CHIRPS schema, as well as a summary of the example application we present in our Usage Notes section. The CHIRPS process involves three main components: i) the Climate Hazards group Precipitation climatology (CHPclim), ii) the satellite-only Climate Hazards group Infrared Precipitation (CHIRP), and iii) the station blending procedure that produces the CHIRPS. We describe the CHPclim, CHIRP, and CHIRPS and present validation analyses based on the GPCC and independent station datasets. We also provide a heuristic example showing how CHIRPS can be used in conjunction with hydrologic models to provide effective early warning of agricultural drought conditions and to assess the hydrologic impacts of low frequency changes in air temperatures and precipitation.
The CHPclim is unique because in addition to the typical physiographic indicators used in most current climatologies (elevation, latitude and longitude), CHPclim also includes information from monthly long-term mean fields from five satellite products: Tropical Rainfall Measuring Mission 2B31 microwave precipitation estimates7, CMORPH microwave-plus-infrared based precipitation estimates15, monthly mean geostationary infrared brightness temperatures21, and land surface temperature estimates22. All products were resampled to a common 0.05 grid.
The CHIRPS station processing stream incorporates data from five public data streams and several private archives. The public data streams are the GHCN monthly, GHCN daily, Global Summary of the Day (GSOD), GTS and Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL, www.sasscalweathernet.org). GTS data are collected daily; GHCN, GSOD and SASSCAL data are updated monthly. Additional observations have been provided by national meteorological agencies, primarily in Mexico, Central America, South America, and sub-Saharan Africa. Currently, the Climate Hazards Group (CHG) archive has almost 600 million daily observations since 1981 and another 600 million before that year from over 200,000 locations. The typical number of observations going into the monthly CHIRPS ranges from greater than 32,000 in the early 1980s to less than 14,000 in 2014, -2.0/diagnostics/stations-perMonth-byRegion/pngs/all.station.count.CHIRPS-v2.0.png. Focusing on all countries except Australia, Brazil, Canada, Colombia, Mexico, and the USA (which provide the largest number of observations), we find a more concerning decline in which the number of observations drops from over 7,000 in 1981 to less than 4,000 after 2010, -2.0/diagnostics/stations-perMonth-byRegion/pngs/Global-top6.station.count.CHIRPS-v2.0.png. In Africa (excluding the Republic of South Africa), we find about 2,400 stations in the early 1980s, and only about 500 after 2010, -2.0/diagnostics/stations-perMonth-byRegion/pngs/Africa.station.count.CHIRPS-v2.0.png. A similar decline can be observed in central southwest Asia ( -2.0/diagnostics/stations-perMonth-byRegion/pngs/Central_Middle_Asia.station.count.CHIRPS-v2.0.png) and South America ( -2.0/diagnostics/stations-perMonth-byRegion/pngs/South_America.station.count.CHIRPS-v2.0.png).
The CHIRPS station blending procedure is a modified inverse distance weighting algorithm that has several unique characteristics. The first of these is the use of the CHIRP to define a local decorrelation distance; this distance is where the estimated point-to-point correlation is zero. Fig. 2 provides examples of the decorrelation distance for February and August in West Africa. To generate these values, time series of CHIRP data were used to calculate the average correlation at a distance of 1.5 for each grid cell. This correlation, and the assumption that the expected correlation is 1 when distance is 0 allows for estimation of a decorrelation slope, which is used to estimate the zero-correlation distance. The correlation structure evolves in space and time, tending to be stronger in areas of heavy well-organized convection.
The CHIRPS data products are derived using approximately 12,000 lines of code written in the Interactive Data Language. While not written as a portable library or toolset, access to the code is not restricted, and it is available upon request.
CHIRPS data are available on our FTP site ( -2.0/) for a number of time steps, spatial domains, and formats from 1981 to present. A preliminary product is produced every pentad with a 2-day lag using GTS and Mexico data only, are also available on these days. At the end of the month, a monthly preliminary CHIRPS is created as the sum of the six preliminary pentads.
CHIRPS data are available on our FTP site ( -2.0/) for a number of time steps, spatial domains, and formats from 1981 to present. A preliminary product is produced every pentad with a 2-day lag using GTS and Mexico data only. The final CHIRPS product is produced once a month with an approximate 3-week lag using all available station data.
CHIRPS data are provided in NetCDF, GeoTiff, and Esri BIL formats. The units are mm per time period, e.g., mm per day, mm per pentad, mm per month. Supporting data are created each month to compliment the CHIRPS data including: i) density of stations used, ii) browse images of Africa for several time steps, iii) lists of locations and names of all stations used for each month, and iv) the number of stations used per month for each country. Please see links at -2.0 and The data reviewed for this paper extend from January 1981 to August 2015. The CHIRPS v2.0 dataset will continue to grow over time as new TIR and station data become available.
The result that temperature change potentially accounted for 50 and 30% of runoff and soil moisture declines, respectively, may have important implications for future impacts of climate change. These estimated temperature influences are shown with purple bars on Fig. 6f,g. Based on the runoff results, warming might have prevented any change in runoff even if the rainfall trend had been in the opposite direction (a wetting trend). Note that such results can be highly model sensitive58, and these findings will need to be verified with more models. We plan future analyses using multiple hydrologic models in conjunction with numerical experiments, isolating precipitation and temperatures impacts through multiple suites of simulations.
Figure 7 shows January-March composites of the large-scale circulation based on the difference between the dry and wet years noted above. The January-March time period shows climate conditions just as rains commence in earnest over southeastern Ethiopia. The purpose of this plot is to show that just before the onset of the rains, the large-scale Indo-Pacific climate system exhibits large coherent anomalies consistent with an intensification of the Walker circulation. Conditions like these can be used by East African climate experts to predict some boreal spring droughts32,43,61. Coupled and uncoupled climate model simulations indicate that when there is a strong tropical SST gradient during boreal winter, with warm SST in the western Pacific and cool SST in the central Pacific, predictable rainfall deficits often follow during East Africa boreal spring rains.
To represent rainfall on longer time scales we make use of a new FEWS NET resource, the Centennial Trends (CenTrends) dataset31; CenTrends covers Eastern Africa and benefits from 217 rainfall stations provided by the Ethiopian national meteorological agency. Standard error estimates from the CenTrends estimation process (kriging) indicates reasonable accuracies back to the 1950s.
Another issue identified in this study is the tendency for the CHIRPS to underestimate the variance in some places, like southwest North America (Fig. 4e), where the satellite-only estimates perform well, in terms of correlation (R=0.70), but heavily underestimate the variance. This may relate to specifics of the frontal precipitation systems characteristic of this region, as well as the fact that the TMPA 3B42 training data used in the CHIRP development process also performs relatively poorly over this region (Fig. 4f). On the other hand, the consistency of the CHIRP inputs and the anomaly-based interpolation process used to incorporate stations helps limit spurious excursions caused by changes in the satellite observation network or precipitation gauge networks.
While improvements to the CHIRPS process are already being developed, the current version 2.0 seems well suited to monitoring droughts in regions where CCD estimates relate reasonably well to observed precipitation systems. Our Ethiopia study provides a good example of CHIRPS utility. Because CHIRPS is produced in near real-time, and October-March southeastern Ethiopia soil moisture exhibits a strong lagged correlation with April-September conditions, October-March CHIRPS data can be used in conjunction with hydrologic models like the VIC to monitor conditions and make successful forecasts (Fig. 8a) that capture most of the recent extreme drought events.
While more work needs to be done to verify the simulation results presented here, and the CHIRPS and CHPclim algorithms will continue to be improved, this paper has described the CHIRPS algorithm, presented some promising validation results, and provided a motivating example application. One substantial weakness of the current CHIRPS algorithm is the lack of uncertainty information provided by the inverse distance weighting algorithm used to blend the CHIRP data and station data. We are currently exploring more rigorous geostatistical models (related to kriging83,84) and plan to use such frameworks to provide standard error fields in future CHIRPS releases. Ongoing research is also exploring mean bias errors with independent validation datasets. This work should lead to improved CHPclim fields, which would improve skill by better capturing the geographic variability of rainfall.
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