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Challenges with availability and quality of climate data in Africa

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Climate data are essential in an array of climate research and applications, that include analyses of climate variability and trends and modelling the impact of climate variability and change on different socioeconomic activities. However, the use of climate data for research and applications in Africa has been scanty because availability of and access to climate data is very limited. In many parts of Africa, weather stations are sparse and their number has been declining. Besides, the distribution of existing stations is uneven, with most located along major roads. Where data exist, they are often of poor quality with many gaps. There are different efforts underway to overcome these challenges. One of these efforts is the ENACTS (Enhancing National Climate Services) initiative. This initiative works with National Meteorological Services in Africa to improve the availability and quality of climate data by combining quality-controlled station observation with satellite and reanalysis proxies.
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Chapter 7
Challenges with availability and quality
of climate data in Africa
Tufa Dinku
International Research Institute for Climate and Society (IRI), The Earth Institute at Columbia University, Palisades, NY, United States
7.1 Introduction
Climate data could support a suite of climate-smart solu-
tions that can reinforce development gains and improve
the lives of those most vulnerable to climate variability
and change. Historical and current weather and climate
observations are essential for many activities that include
operational meteorology, identifying extreme events and
assessing associated risks, development of climate-
informed early warning systems, planning, and research.
Climate observations are also used as a baseline for
assessing changes in climate, for providing the initial con-
ditions and evaluating climate predictions (Manton et al.,
2010), as well as assessing the impact of climate-sensitive
interventions (Thomson et al., 2017).
It may be argued that the need for quality climate data is
more urgent in Africa owing to serous vulnerabilities to
climate variability and change. The Intergovernmental
Panel on Climate Change (IPCC, 2014) reports that Africa
is one of the most vulnerable continents to climate change
due to high exposure to climate stress and low adaptive
capacity. For instance, most of southern Africa has been
experiencing increase in annual temperature over most of
the region during the last half of the 20th century
(Cervigni et al., 2015). This is important because rain-fed
agriculture generates a significant portion of the national
gross domestic product (GDP) and provides livelihoods
for a large percentage of the rural population in Africa.
While approximately 80% of global agriculture is rain
fed, this figure may reach up to 95% in some areas of Africa
(Alexandratos and Bruinsma, 2012). The fact that 60%
70% of the population is dependent, directly or indirectly,
on increased agricultural productivity makes the impacts
of weather and climate very significant (Pinstrup-
Andersen et al., 1999).
However, use of climate data and information products
has been very limited in Africa owing to lack of useful infor-
mation or challenges of accessing existing data. Currently,
the primary sources of climate observations are the National
Meteorological and Hydrological Services (NMHS). These
data come mainly from classical conventional stations (syn-
optic, climatological, agrometeorological, and rainfall sta-
tions) in each country. The main strength of these station
observations is that they are assumed to give the “true” mea-
surements of the climate variable of interest. However, in
many parts of Africa, station coverage is very sparse has
been declining (Dinku et al., 2017;Washington et al.,
2006). This has resulted in critical gaps in the availability
both historical climate data as well as current observations.
This problem has been compounded by the fact that the dis-
tribution of existing stations is uneven, with most weather
stations located in cities and towns along major roads. As
a result, coverage tends to be worse in rural areas, exactly
where livelihoods may be most vulnerable to climate
variability and climate change. Where station records do
exist, they are often of poor quality with many missing
observations.
There have been some efforts to overcome scarcity of
historical data in Africa, and many other parts of the world.
These include interpolation of observations from existing
stations as well as the use of proxies such as satellite
rainfall estimate. The Enhancing National Climate Ser-
vices (ENACTS) initiative of the International Research
Institute for Climate and Society (IRI) of the Columbia
University is using an approach that strives to make
optimum uses of all station observations and proxies. This
is accomplished by working directly with the National
Meteorological Services in Africa to combine quality-
controlled data from national observation networks with
satellite estimates for rainfall and climate model reanalysis
products for temperature.
The following section will discuss the challenges of data
availability in detail with some examples, while Section 7.3
will focus on data quality issues. Section 7.4 presents some
efforts aimed at alleviating the challenges of data avail-
ability and quality.
Extreme Hydrology and Climate Variability.https://doi.org/10.1016/B978-0-12-815998-9.00007-5
©2019 Elsevier Inc. All rights reserved. 71
7.2 Challenges to availability and quality of
climate data in Africa
7.2.1 Sparse and declining observation
network
Data sparsity may refer to the lack of data to create useful
climate information needed to conduct meaningful analysis
and to inform climate resilient development. Generally, data
sparsity refers to the situation where climate data are not
available or accessible. While this problem is prevalent
globally, it is particularly common in Africa, especially over
areas where there are difficult and remote geographies, con-
flict, and investment in data is a relatively low priority. The
primary sources of climate observations are the NMHS in
each country. However, the observation network in Africa
is seriously inadequate, with the number and quality of
weather stations in many parts of the continent in decline
(Dinku et al., 2017; Parker et al., 2011;Washington et al.,
2006;Malhi and Wright, 2004). Existing stations are
unevenly distributed with most of the stations located in
cities and towns along the main roads. As a result, climate
data may not be available over rural areas where, one can
argue, these data are needed most. Fig. 7.1 shows the
number of stations per a 0.5°grid box used in the “full data”
gridded rainfall product of the Global Precipitation Clima-
tology Center (GPCC; Becker et al., 2013). The map depicts
uneven distribution of stations with higher densities over
Europe. There is also variability within Africa itself, with
very few stations over the forested and dessert parts of
the continent (marked areas on the map). These are not
the only rainfall observation stations available in Africa,
but Fig. 7.1 does represent the relative distributions of
station over Africa very well.
Sparse station distribution is not the only challenge; the
number of observation station has also been declining for
decades. Fig. 7.2 shows time series of average number of sta-
tions used in the GPCC full-data product over Africa since
1901. The number of observing stations shows steady decline
since the early 1980s. This decline may be attributed to two
factors: the first one could be that the data are available but
may not have been provided to the GPCC; and the second
factor is actual decline in observation. A look at time series
of data available at some NMHS may provide an insight into
which factor is more important. Fig. 7.3 presents the time
series of number station for Uganda starting from 1901.
The declining trend is very similar to that of Fig. 7.2. These
numbers are calculated from information on the dates sta-
tions were opened and closed. Thus, data availability may
slightly be different because a station could have stopped
reporting before it was closed. The time series in Fig. 7.4, this
time from Mali, is calculated from actual available data.
Again, this confirms that the declines are real and universal,
at least for Africa.
7.2.1.1 Factors contributing to data sparseness
and declining observation networks
There are a number of factors that has contributed to the
sparse observation network and the decline in the number
weather stations over many parts of Africa. The major ones
include declining investment, social or political conflict,
and difficult and remote geography.
Declining investment in climate infrastructure is a major
impediment to operation and maintenance of climate obser-
vation networks and related infrastructure for many NMHS
in Africa. This could in part be due to difficulties in articu-
lating the value added by the NMHS (Rogers and
Tsirkunov, 2010), and sometimes lack understanding the
benefits climate observations for the development
(Hansen et al., 2007). The declines shown in Figs. 7.27.4
could at least partly be ascribed to this factor. However,
Fig. 7.5 from Madagascar may provide a more dramatic
picture. In the 30 years from 1971 to 2001, the average
number of active stations in Madagascar declined from over
400 to under 50. This is a very serious loss and challenge to
use of climate data for research and different applications.
60N
50N
40N
30N
20N
10N
EQ
10S
20S
30S
20W 10W 0 10E 20E 30E 40E 50E 60E
0
1
2
3
4
6
8
10
15
20
30
50
100
FIG. 7.1 Number of stations per 0.5°grid box for July 2013 used in
Global Precipitation Climatology Project (GPCC) Full Data gridded
rainfall product Version 7. Map is generated using GPCC’s Visualizer,
https://kunden.dwd.de/GPCC/Visualizer.
72 Extreme hydrology and climate variability
FIG. 7.2 Time series of average number of stations used in the GPCC full-data product over Africa (15°W45°E and 30°S30°N). (Data from GPCC,
https://www.dwd.de/EN/ourservices/gpcc/gpcc.html.)
500
450
400
350
300
250
200
150
100
50
0
1902
1905
1908
1911
1914
1917
1920
1923
1926
1929
1932
1935
1938
1941
1944
1947
1950
1953
1956
1959
Yea r
1962
1965
1968
1971
1974
1977
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
2010
2013
Average number of stations
FIG. 7.3 Time series of average number of stations reporting each year for Uganda.
FIG. 7.4 Time series of average number of stations reporting each year for Mali.
Conflict or political upheavals can also result in the dis-
ruption of observation networks, and thus data sparsity. An
analysis by Auffhammer et al. (2013) has demonstrated
relationship between conflict and the number of missing
observations. This has been a serious challenge particularly
for Africa for decades with different parts of the continent
experiencing conflicts in one form or another. These con-
flicts have led to loss of data and disruption of observations.
A very informative example would be the case of Rwanda
(Fig. 7.6). Leading and following the 1994 genocide in
Rwanda, the meteorological observation network was dev-
astated. It took the country nearly 15 years to return its
observation network to pre-conflict level. The observation
during that period has been lost forever. There are many
such examples in Africa.
Difficult geography and terrain contribute to the sparse
distribution of the observation network in many parts of the
continent. Mountains, forest, and desert areas make instal-
lation and maintenance of observation network difficult,
limiting the number of observation stations. For example,
both the Sahara Desert and the Congo River basin lack
meaningful density of reporting stations (Fig. 7.1). A related
factor is the dispersed nature of rural populations, particu-
larly over the lowland areas. Given the concentration of
people and economic activity in cities, the marginal benefit
of an additional observation station near a city may be con-
sidered larger to national planners than one located in a rural
community, despite the implications for achieving a more
complete spatial coverage. Along with geographic consider-
ations, this skews the construction of new stations toward
urban centers and away from rural and/or difficult to access
areas. The distribution of weather station over Kenya
(Fig. 7.7), with relatively better station coverage over more
populated highland areas and very sparse distribution over
the lowlands could be a good example.
7.2.2 Challenges to accessibility
available data
Even when data exist, accessing data from the NMHS in
Africa may not be trivial. Sharing of data with other institu-
tions, within or outside the country, is quite limited and
FIG. 7.5 Time series of average number of stations reporting each year for Madagascar.
100
90
80
70
60
50
Number of stations
40
30
20
10
Year
0
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
FIG. 7.6 Time series of average number of stations reporting each year
for Rwanda.
5000
4000
3000
2000
1000
34
–4
–2
0
Latitude
2
4
36 38
Lon
g
itude
40
0
FIG. 7.7 Distribution of all raingauges stations over Kenya.
74 Extreme hydrology and climate variability
often includes fees for provision when available (Overpeck
et al., 2011). Although all NMHSs are mandated to share
national data via World Meteorological Organization
(WMO’s) Global Telecommunication System (GTS), the
data available from Africa are typically only a small subset
of the total number of stations and data types managed by
the NMHS. For instance, each October, the WMO carries
out their Annual Global Monitoring (AGM) survey to assess
the reception of certain datasets from the different WMO
Regional Associations. Fig. 7.8, which shows the results
from that survey, clearly demonstrates Africa’s challenges
relative to the other continents. When comparing the per-
centage of climate data reports received by the WMO during
200417 with the number of reports required by the WMO
from the different regions, Africa’s contribution, at less than
50% of what is expected, is the smallest. And this includes
South Africa, where the density of stations is significantly
higher than those of other countries and thus raises the
average.
There are several factors that contribute to challenge of
data accessibility, which include legal restrictions, low
financial investment, lack of dissemination capacity and
tools, and high access costs. In some cases, the historical
records are available on paper or microfiche, but have not
yet been digitized, reducing its accessibility. There is also
simple mistrust on the part of the NMHS of how meteoro-
logical data might be used (or misused) without their
consent. The NMHS may also wish to exercise control over
their data and may not be happy if, for instance, the private
sector or universities share their data with third parties. The
NMHS concern here is that this may undermine demand for
or attention to their products and may serve to potentially
lead to private profiteering at their expenses. In many other
cases, data restrictions may stem from the need to charge
fees to cover the cost of preparing and sharing data.
7.2.3 Challenges of data quality
In addition to the challenges related to availability and
accessibility of climate data, the quality of the accessible
data is another serious challenge. Low data quality may
be characterized by poor accuracy or precision as well as
missing observations. Station measurements are prone to
human, instrumental, and other measurement errors. The
other source of error, which occurs very often, happens
during entering data into computers. Lack of tools to
perform quality control or lack capacity to use existing tools
is also another contributing factor. For instance, many
Africa NMHS do have climate database management
systems that include tools for basic quality control.
However, in many instances, these tools may not be used
or may not be used properly.
Figs. 7.97.11 provide some actual example of data
quality issues. These are outputs from a quality control pro-
cedure data during implementation of the ENACTS (Dinku
et al., 2017) in the different countries. Red bars show suspi-
cious data values. In Fig. 7.9, temperature values in degree
Fahrenheit are mixed up with a database that uses degree
Celsius. This NMHS has switched from Fahrenheit to
Celsius, but somehow forgot to convert Fahrenheit value
into Celsius for some observations. The example in
Fig. 7.10 is a case where rainfall data are entered instead
of minimum temperatures. And Fig. 7.11 is a good example
of data entry error, where a missing period resulted in a
FIG. 7.8 Percentage of minimum required reports from regional climatological networks achieved by region. (Data from WMO’s Annual Global Mon-
itoring report, http://www.wmo.int/pages/prog/www/ois/monitor/index_en.html.)
Challenges with availability and quality of climate data in Africa Chapter 775
1947
20 30 40
Daily maximum temperature (°C)
50 60 70
1952 1957 1962 1967 1972 1977 1982
January
1987 1992 1997 2002 2007 2012
FIG. 7.9 Data quality check: temperature in degree Fahrenheit is mixed with a database in degree Celsius (red bar).
December
1981
0510
Daily minimum temperature (°C)
15
1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 2014
FIG. 7.10 Data quality check: rainfall data entered in place of minimum temperature for 1984.
FIG. 7.11 Data quality check: typing error during data entry.
76 Extreme hydrology and climate variability
temperature value of almost 300 °C! These are serious
issues and, even though isolated cases, can affect statistics
(such as the mean temperature for that stations) computed
from these data.
7.3 Alleviating challenges data availability
and quality
As noted in the previous section, challenges to the avail-
ability of climate data arise due to scanty and/or deterio-
rating observation networks and insufficient technical
capacities by the NMHS. Overcoming the challenge would
require addressing these issues. There are both short- and
long-term solutions to these challenges. The short-term
solutions are approaches used to overcome sparsity of his-
torical data in order to use the data for research and different
applications. The long-term solutions aim at addressing the
challenges of data availability in future by strengthening the
capacities of NMHS as well as through direct efforts by dif-
ferent parties to expand collection and dissemination of
weather data. The focus here will be on efforts that try to
alleviate availability of historical data.
7.3.1 Gridded station data and satellite
proxies
There are different approaches adopted to overcome
scarcity of historical data. One of these approaches focuses
on making best use of available observations through spatial
interpolation. There are now number of gridded datasets
generated by interpolating station measurements onto
regular grids, and the most widely used ones include GPCC
(Becker et al., 2013) and the gridded products from the
Climate Research Unit at the University of East Anglia
(Harris et al., 2014;Mitchell and Jones, 2005). The quality
of the gridded products ultimately depends on the number,
spatial distribution, and quality of the observations used.
The fact that the observation network in Africa has been
deteriorating also means that the qualities of the gridded
products created from these observations have also been
deteriorating. Thus, one needs to be extremely careful when
using these products. This is particularly true for application
such as trend analysis because the year-to-year variations
may be influenced as much by the inhomogeneity of the
time series as by actual changes in rainfall amount.
Satellite-derived products have also been used increas-
ingly to complement or in place of station observations. Sat-
ellite rainfall estimates are the most widely used. The main
strengths of satellite rainfall estimates include near global
coverage, improved temporal and spatial resolution, and
the products are freely available from different global
centers. There are now a few satellite-based rainfall
products that provide over 30 years of rainfall time series.
These include the African Rainfall Climatology version 2
(ARC2, Novella and Thiaw, 2013), and the Tropical Appli-
cations of Meteorology using SATellite and ground-based
observations (TAMSAT) rainfall estimate (Maidment
et al., 2014). These products have relatively high spatial
(0.1°and 0.0375°, respectively) and temporal (daily) reso-
lutions, and are created specifically for Africa. The two
products are also based exclusively on thermal infrared
(TIR) data, which makes their time series relatively con-
sistent overtime. However, ARC2 uses stations data
obtained through GTS, which might introduce some incon-
sistencies since the density of these observations can vary
substantially overtime (e.g., Maidment et al., 2015).
Another approach is combining data from different
sources. This mainly involves using different approaches
to combine satellite rainfall estimates and station observa-
tions. Many of the current operational satellite rainfall
products do use station data for calibrating the retrieval
algorithms (e.g., TAMAST) and/or combine station data
with satellite estimates. However, in many cases, the only
available observations are those obtained through GTS,
which are very limited particularly over Africa. There are
some satellite-based products that use many more stations.
One of these products is the Climate Hazards Group Infrared
Precipitation combined with station data (CHIRPS, Funk
and Coauthors, 2015). CHIRPS is a relatively new product
that uses many stations when available. It has relatively
good spatial (0.05°latitude/longitude) and temporal (daily,
pentad, and dekadal) resolutions, a quasi-global coverage
(50°S50°N), and the time series goes back to 1981. The
CHIRPS product may have some inhomogeneity because
the availability of station data, particularly over Africa, is
not consistent overtime.
7.3.2 The ENACTS approach
The ENACTS program is an effort led by the IRI at the
Columbia University to improve the availability, access,
and use of climate data and derived information products
for research and applications (Dinku et al., 2013, 2014a,b,
2017). Fig. 7.12 represents the three main elements (pillars)
of the ENACTS, which are improving availability,
enhancing access, and promoting use of climate data and
information products. The ENACTS team works directly
with the NMHS in Africa to organize and quality control
all data from the national observation network, and combine
the quality-controlled data with proxies. The engagement
with the NMHS is critical to this approach as the NMHS
is the nationally mandated organizations for the creation,
management, and dissemination of meteorological obser-
vation and is the custodians of the historical records.
The ENACTS approach focuses on the creation of tem-
porally and spatially complete reliable climate data and
information products. The first step is quality control of
Challenges with availability and quality of climate data in Africa Chapter 777
station data, which involves checking station location, iden-
tifying outliers, and checking and fixing breaks in station
time series. The quality-controlled station data are then
combined with satellite estimates for rainfall or climate
model reanalysis products for temperature (Fig. 7.13).
The main advantage of the satellite and reanalysis products
is that they offer spatially complete data and are freely
available. Satellite rainfall estimates now go back over
30 years while reanalysis products go even further. As a
result, the ENA CTS datasets cover over 30 years of rainfall
and temperature (minimum and maximum) time series at
spatial resolution of 0.0375°and daily or dekadal (10-
day) time scales. The quality of the final products depends
mainly on the number, spatial distribution, and the quality of
the station data.
The main strength of the ENACTS approach is that it
makes use all available station data most of which are not
available outside the country. This is because the team
works directly with the NMHS. For instance, Fig. 7.14 com-
pares the station used in the ENACTS and those used in
CHIRPS. Compared to other satellite-derived products,
Build capacity of NMHS
Quality control station
data
Create mechanisms for
data sharing
Install IRI Data Library
The three pillars of enacts
Engage users:
Raise awareness
Involve users in product
development
Build capacity of users
to understand and use
climate information
Develop online tools for
data analysis and
visualization
Combine station data
with proxies
Improve seasonal
forecast
Improve availability Enhance access Promote use
FIG. 7.12 The three elements (pillars) of the ENACTS.
24
22
20
18
16
14
12
10
–10 –5
Longitude
0–10
025 50 75 100
10
12
14
16
18
20
22
24
150
–5
Longitude
Station Satellite Combined
Rainfall
(
mm
)
Latitude
0
FIG. 7.13 An example of combining station observation (left) with satellite rainfall estimate (center) to generate a blended product (right) over Mali in
West Africa.
FIG. 7.14 Comparison of stations used in the ENACTS (o) and CHIRPS
(*) data products.
78 Extreme hydrology and climate variability
CHIRPS uses the most number of stations. However,
Fig. 7.14 shows that the number of stations used in CHIRPS
are a small fraction of those used by the ENACTS.
The ENACTS approach also enhances access to climate
information through an interactive web-based platform
(maprooms). The Maprooms support analyses, visuali-
zation, and download of an array of derived climate infor-
mation products (Fig. 7.15) and include analysis of past
climate, monitoring of current climate, and climate fore-
casts. Users can extract location-specific products for any
selected grid cell, a box, administrative polygon, or
water shade.
The ENACTS approach has been implemented at 12
NMHS and two Regional Climate Centers in Africa.
7.4 Summary
The use of climate data and derived information products
for research and applications in Africa is very limited
because of serious challenges with regards to the avail-
ability of and access to climate data. The distribution of
weather stations across the continent is very sparse. This
has resulted in critical gaps in the availability of historical
climate data, particularly over rural areas where coverage
tends to be worse. The number of observing stations has also
been declining for decades. There are a number of factors
that have been contributing to these challenges. The major
factors include declining investment in the NMHS, social or
political conflict, and difficult and remote geography.
Accessing data is another challenge even when the data
exist. Although all NMHSs are mandated to share data
nationally and globally, the data available from African
NMHS are typically only a small fraction of data available
within the NMHS. Factors that contribute to this challenge
include legal restrictions (data policy), low financial
investment, lack of dissemination capacity and tools, and
high access costs. Even when accessible, the quality of
the data has been found to be the other serious challenge.
Poor data quality may be ascribed to lack of tools for quality
control as well as lack of capacity to use existing tools.
Different efforts have been made to alleviate the chal-
lenges of data availability and access. These include inter-
polation of existing stations observations and use of proxies
such as satellite rainfall estimates and climate model reana-
lysis products. The ENACTS initiative of the IRI, Columbia
University, is a new approach that strives to improve data
availability, quality, and access by blending station
quality-controlled data from the whole national observation
network with proxies such as satellite rainfall estimates and
climate model reanalysis products. The ENACTS delivers
robust climate data and targeted information products spe-
cifically relevant to the needs of decision makers at multiple
levels, empowering a diverse range of actors to use past,
present, and future climate information. As a result, the
FIG. 7.15 The ENACTS “maprooms” that provides online access to an array of climate information products for different applications (http://www.
ethiometmaprooms.gov.et:8082/maproom/).
Challenges with availability and quality of climate data in Africa Chapter 779
ENACTS supports a suite of climate-smart solutions that
reinforce development gains and improve the lives of those
most vulnerable to climate variability and change.
Acknowledgment
We would like to thank the National Meteorological Services of
Kenya, Madagascar, Mali, and Rwanda who provided the data used
to create some of the plots.
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80 Extreme hydrology and climate variability
... Significant efforts and advancements in technology have resulted in increased availability of WCI (Dinku et al., 2014;Hewitt et al., 2020). However, this has not translated to improved accessibility, especially across user groups (practitioners and communities) in Africa where varied access to WCI is noted (Dinku, 2019;Vaughan et al., 2019). In addition, even if WCI is available and accessible, this does not necessarily mean the information is used to inform local decisions as it may not address the information needs of specific users (Vaughan and Dessai, 2014;Naab et al., 2019). ...
... Essentially, decision-makers and information producers/providers ("providers" henceforth) require access to quality and credible "scientific" data and information to be able to fulfil the information needs of the users and manage the potential risks (Hewitt et al., 2020). But the required data and information is often limited (Van Den Homberg et al., 2017) or inaccessible (Susha et al., 2017;Dinku, 2019). In their framework, Van Den Homberg et al. (2017) notes that being data-prepared can help reduce the impacts associated with extreme events if high-quality data that meets the information needs of the providers are accessible before the disaster hits. ...
... These factors will however vary according to the context. For example, a study by Dinku (2019) found that the availability and completeness of climate data vary across Africa due to the scarcity of weather stations. In addition, the limited accessibility of available data has been attributed to the legal regulations that govern how institutions share data as well as the high costs levied to access the data. ...
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... Many studies, including the Intergovernmental Panel on Climate Change (IPCC) report [30], have discussed or noted the challenges of climate change modelling for past and future scenarios. Some of the major limitations-especially in Africa-are related to data quality, availability, and accessibility [31][32][33]. The paucity of data sources directly affects research in the assessment of climatic conditions and changes, which directly impacts livelihoods [14,34]. ...
... One of the primary sources of most climate data in Africa is a network of weather stations, which are scattered disproportionately across the landscape. The continent has a very low density of weather stations, with data not readily available [31]. In addition, the historical data gathered from this network span but a few decades and the records are typically riddled with missing information, incorrect capture, and incomplete conversion between the metric and imperial system. ...
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... The availability, quality and timeliness of these services, however, are in turn dictated by the availability, quality, and timeliness of the underlying climate data that form their foundation. In many developing countries, especially those in Africa, these aspects have been negatively affected by low investment in meteorological services, resulting in inadequate, poorly maintained, and unevenly distributed stations that give rise to climate data of inconsistent and poor quality (Washington et al., 2006;Dinku et al., 2018;Dinku, 2019). ...
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Climate data are essential in an array of climate research and applications. Climate data also provide the foundation for the provision of climate services. However, in many parts of Africa, weather stations are sparse, and their numbers have been declining over the last half-century. Moreover, the distribution of existing meteorological stations is uneven, with most weather stations located in towns and cities along the main roads. To address these data gaps, efforts over the last decade, largely driven by external donor funding, have focused on expanding meteorological observation networks in many parts of Africa, mainly through the provision of Automatic Weather Stations (AWS) to National Meteorological Services (NMS). While AWS offer a number of advantages over the conventional ones, which include automated reporting at a very fine temporal resolution (15 min, on average), they also have several disadvantages and accompanying challenges to their use. Some of these well-known challenges are the high maintenance requirements and associated costs that arise from the need to procure replacement parts that may not be available locally. However, another major, under-discussed challenge confronting NMS is the disparities between the different station types provided by different donors that has given rise to barriers to pragmatically using the plethora of data collected by AWS in decision-making processes. These disparities include major differences in the way the data from various AWS types are formatted and stored, which result in poorly coordinated, fragmented, and unharmonized datasets coming from different AWS networks. The end result is that while top-of-the-line AWS networks may systematically be collecting highly needed data, the inability of NMS to efficiently, combine, synchronize, and otherwise integrate these data coherently in their databases limits their use. To address these challenges, a free web-based application called Automatic Weather Station Data Tool (ADT) with an easy-to-use graphical user interface was developed to help NMS to access, process, perform quality control, and visualize data from different AWS networks in one place. Now implemented in five African countries (Ethiopia, Ghana, Kenya, Rwanda, and Zambia), ADT also enables real-time monitoring of stations to see which ones are working and which ones are offline. This tool emerged from a wider climate services approach, the Enhancing National Climate Services (ENACTS), recognizing that availability of high-quality climate data does not automatically translate to ease of access or effective use.
... The problem of data availability is the result of a sparse network of measurement stations, little investment in measurement infrastructure, and, most importantly, a lack of understanding of the benefits of climate observations for development [36]. This problem is even more pronounced in hard-to-reach and geographically remote areas. ...
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The paper compares air and sea surface temperatures in recent years on two islands in the Adriatic Sea. The data measured at the climatological station Krk on the island of Krk and the main meteorological station Lastovo on the island of Lastovo are used. The island of Krk is located in the north of the Adriatic Sea and Lastovo in the south. Since a significant increase in air and sea surface temperatures has been observed over the last thirty years, the goal is to establish how they reflect at these two stations, 313 km apart. The goal of the analysis is to monitor the changes in these two islands to reduce the negative impacts they may cause. The analysis of sea temperatures showed that global warming has a greater impact in the northern Adriatic than in the southern Adriatic. Air and sea surface temperatures have a faster upward trend on Krk than on Lastovo. Similar to the Mediterranean Sea, a positive trend was observed in the Adriatic Sea for both sea surface temperature and air temperature.
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As a crucially-needed adaptation to climate change, the United Nations plans to expand Early Warning Systems (EWS) for extreme weather to cover everyone on Earth. Given the growing interest in this climate change adaptation solution, we assess how well weather early warnings perform for extreme events in different parts of the world. First, we carry out a forecast verification for weather forecasts from the National Oceanic and Atmospheric Administration (NOAA) for 95th percentile extreme heat and extreme precipitation globally at 0.5° resolution, with three days of lead time. We present the results alongside similar verification results from ECMWF forecasts and a CHIRPS-GEFS forecast, to identify regions of the world with consistent forecast skill. We then overlay the skill of these short-term weather forecasts on top of climate change projections for the increasing frequency of the extreme events themselves. Based on these results, we offer policy implications for EWS investments in different regions. We find that in much of the tropics, weather forecasts have relatively poor skill in forecasting extreme temperature and precipitation events, calling for further investments in predictability. In the extra-tropics, most extreme heat and extreme precipitation events can be correctly forecasted, with better results for multi-day events and shorter lead-times. While there is room to improve predictability, end-to-end investments in EWS in these regions can focus on the use of existing skillful forecasts. Finally, most of the world's land area is projected to see an increase in the magnitude of extreme heat and precipitation events with climate change, and EWS investments in these regions should prepare for unprecedented extremes and changing vulnerabilities. These results provide a foundation for localized research on EWS in different parts of the world as well as evidence for policy and donors on how best to invest in EWS in different regions.
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Chapter
Advances in agricultural data production provide ever-increasing opportunities for pushing the research frontier in agricultural economics and designing better agricultural policy. As new technologies present opportunities to create new and integrated data sources, researchers face tradeoffs in survey design that may reduce measurement error or increase coverage. In this chapter, we first review the econometric and survey methodology literatures that focus on the sources of measurement error and coverage bias in agricultural data collection. Second, we provide examples of how agricultural data structure affects testable empirical models. Finally, we review the challenges and opportunities offered by technological innovation to meet old and new data demands and address key empirical questions, focusing on the scalable data innovations of greatest potential impact for empirical methods and research.
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In the topographic complex catchments, landscape features have a significant impact on the spatial prediction of rainfall and temperature. In this study, performance assessments were made of various interpolation techniques for the prediction of the spatial distribution of rainfall and temperature in the Mille and Akaki River catchments, Ethiopia, through an improved approach on selecting the auxiliary variables as a covariate. Two geostatistical interpolation techniques, ordinary kriging (OK) and kriging with external drift (KED), and one deterministic interpolation technique, inverse distance weighting (IDW), were tested through a leave-one-out cross-validation (LOOCV) procedure. The results indicated that using the multivariate geostatistical interpolation technique (KED) with the auxiliary variables as a covariate outperformed the univariate geostatistical (OK) and deterministic (IDW) techniques for the spatial interpolation of sampled rainfall–temperature data in both contrasting catchments, Akaki and Mille, with the lowest estimation errors (e.g., for Mille annual mean rainfall: root mean square error=75.32, 77.34, 245.72, mean bias error=3.70, −33.18, −15.61, mean absolute error=67.99, 69.51, 192.64) using KED with the combination of elevation and easting as a covariate, IDW and OK, respectively. Thus, the study confirmed that the use of elevation and easting/northing coordinates as predictors in geostatistical interpolation techniques could significantly improve the spatial prediction of climatic variables.
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Since 2010, the Roll Back Malaria (RBM) Partnership, including National Malaria Control Programs, donor agencies (e.g., President's Malaria Initiative and Global Fund), and other stakeholders have been evaluating the impact of scaling up malaria control interventions on all-cause under-five mortality in several countries in sub-Saharan Africa. The evaluation framework assesses whether the deployed interventions have had an impact on malaria morbidity and mortality and requires consideration of potential nonintervention influencers of transmission, such as drought/floods or higher temperatures. Herein, we assess the likely effect of climate on the assessment of the impact malaria interventions in 10 priority countries/regions in eastern, western, and southern Africa for the President's Malaria Initiative. We used newly available quality controlled Enhanced National Climate Services rainfall and temperature products as well as global climate products to investigate likely impacts of climate on malaria evaluations and test the assumption that changing the baseline period can significantly impact on the influence of climate in the assessment of interventions. Based on current baseline periods used in national malaria impact assessments, we identify three countries/regions where current evaluations may overestimate the impact of interventions (Tanzania, Zanzibar, Uganda) and three countries where current malaria evaluations may underestimate the impact of interventions (Mali, Senegal and Ethiopia). In four countries (Rwanda, Malawi, Mozambique, and Angola) there was no strong difference in climate suitability for malaria in the pre- and post-intervention period. In part, this may be due to data quality and analysis issues.
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This book evaluates -using for the first time a single consistent methodology and the state-of-the-arte climate scenarios-, the impacts of climate change on hydro-power and irrigation expansion plans in Africa’s main rivers basins (Niger, Senegal, Volta, Congo, Nile, Zambezi, Orange); and outlines an approach to reduce climate risks through suitable adjustments to the planning and design process. The book finds that failure to integrate climate change in the planning and design of power and water infrastructure could entail, in scenarios of drying climate conditions, losses of hydropower revenues between 5% and 60% (depending on the basin); and increases in consumer expenditure for energy up to 3 times the corresponding baseline values. In in wet climate scenarios, business-as-usual infrastructure development could lead to foregone revenues in the range of 15% to 130% of the baseline, to the extent that the larger volume of precipitation is not used to expand the production of hydropower. Despite the large uncertainty on whether drier or wetter conditions will prevail in the future in Africa, the book finds that by modifying existing investment plans to explicitly handle the risk of large climate swings, can cut in half or more the cost that would accrue by building infrastructure on the basis of the climate of the past.
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The availability of highly accessible and reliable monthly gridded data sets of global land-surface precipitation is a need that was already identified in the mid-1980s when there was a complete lack of globally homogeneous gauge-based precipitation analyses. Since 1989, the Global Precipitation Climatology Centre (GPCC) has built up its unique capacity to assemble, quality assure, and analyse rain gauge data gathered from all over the world. The resulting database has exceeded 200 yr in temporal coverage and has acquired data from more than 85 000 stations worldwide. Based on this database, this paper provides the reference publication for the four globally gridded monthly precipitation products of the GPCC, covering a 111-yr analysis period from 1901–present. As required for a reference publication, the content of the product portfolio, as well as the underlying methodologies to process and interpolate are detailed. Moreover, we provide information on the systematic and statistical errors associated with the data products. Finally, sample applications provide potential users of GPCC data products with suitable advice on capabilities and constraints of the gridded data sets. In doing so, the capabilities to access El Niño–Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO) sensitive precipitation regions and to perform trend analyses across the past 110 yr are demonstrated. The four gridded products, i.e. the Climatology (CLIM) V2011, the Full Data Reanalysis (FD) V6, the Monitoring Product (MP) V4, and the First Guess Product (FG), are publicly available on easily accessible latitude/longitude grids encoded in zipped clear text ASCII files for subsequent visualization and download through the GPCC download gate hosted on ftp://ftp.dwd.de/pub/data/gpcc/html/download_gate.html by the Deutscher Wetterdienst (DWD), Offenbach, Germany. Depending on the product, four (0.25°, 0.5°, 1.0°, 2.5° for CLIM), three (0.5°, 1.0°, 2.5°, for FD), two (1.0°, 2.5° for MP) or one (1.0° for FG) resolution is provided, and for each product a DOI reference is provided allowing for public user access to the products. A preliminary description of the scope of a fifth product – the Homogenized Precipitation Analysis (HOMPRA) – is also provided. Its comprehensive description will be submitted later in an extra paper upon completion of this data product. DOIs of the gridded data sets examined are as follows: doi:10.5676/DWD_GPCC/CLIM_M_V2011_025, doi:10.5676/DWD_GPCC/CLIM_M_V2011_050, doi:10.5676/DWD_GPCC/CLIM_M_V2011_100, doi:10.5676/DWD_GPCC/CLIM_M_V2011_250, doi:10.5676/DWD_GPCC/FD_M_V6_050, doi:10.5676/DWD_GPCC/FD_M_V6_100, doi:10.5676/DWD_GPCC/FD_M_V6_250, doi:10.5676/DWD_GPCC/MP_M_V4_100, doi:10.5676/DWD_GPCC/MP_M_V4_250, doi:10.5676/DWD_GPCC/FG_M_100.
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The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset builds on previous approaches to ‘smart’ interpolation techniques and high resolution, long period of record precipitation estimates based on infrared Cold Cloud Duration (CCD) observations. The algorithm i) is built around a 0.05° climatology that incorporates satellite information to represent sparsely gauged locations, ii) incorporates daily, pentadal, and monthly 1981-present 0.05° CCD-based precipitation estimates, iii) blends station data to produce a preliminary information product with a latency of about 2 days and a final product with an average latency of about 3 weeks, and iv) uses a novel blending procedure incorporating the spatial correlation structure of CCD-estimates to assign interpolation weights. We present the CHIRPS algorithm, global and regional validation results, and show how CHIRPS can be used to quantify the hydrologic impacts of decreasing precipitation and rising air temperatures in the Greater Horn of Africa. Using the Variable Infiltration Capacity model, we show that CHIRPS can support effective hydrologic forecasts and trend analyses in southeastern Ethiopia.
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Multiple observational data sets and atmosphere-only simulations from the Coupled Model Intercomparison Project Phase 5 are analyzed to characterize recent rainfall variability and trends over Africa focusing on 1983–2010. Data sets exhibiting spurious variability, linked in part to a reduction in rain gauge density, were identified. The remaining observations display coherent increases in annual Sahel rainfall (29 to 43 mm yr-1 per decade), decreases in March–May East African rainfall (-14 to -65 mm yr-1 per decade), and increases in annual Southern Africa rainfall (32 to 41 mm yr-1 per decade). However, Central Africa annual rainfall trends vary in sign (-10 to +39 mm yr-1 per decade). For Southern Africa, observed and sea surface temperature (SST)-forced model simulated rainfall variability are significantly correlated (r~0.5) and linked to SST patterns associated with recent strengthening of the Pacific Walker circulation.
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Sound climate risk management requires access to the best available decision-relevant climate information and the ability to use such information effectively. The availability and access of such information and the ability to use it is challenging, particularly throughout rural Africa. A gap analysis published by the International Research Institute for Climate and Society (IRI) and the Global Climate Observing System (GCOS) in 2005 explored these challenges in detail and identified four key gaps: (i) gaps in integration of climate into policy; (ii) gaps in integration of climate into practice at scale; (iii) gaps in climate services; and (iv) gaps in climate data. Though this document was published nearly nine years ago, the gaps it highlighted are still relevant today. In the last decade, IRI has been making efforts to address these critical issues in a systematic way through projects and partnerships in Africa. This paper describes IRI’s efforts in Ethiopia, a country particularly prone to climate related risks. Here we outline a creative solution to bridge the gaps in the availability, access and use of national climate information through the Enhancing National Climate Services (ENACTS) initiative. We then discuss how policy and practice has changed as a result of IRI engagement in the development of climate services in the water, public health and agricultural sectors. The work in Ethiopia is indicative of the efforts IRI is implementing in other countries in Africa and in other parts of the world.
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African societies are dependent on rainfall for agricultural and other water-dependent activities, yet rainfall is extremely variable in both space and time and reoccurring water shocks, such as drought, can have considerable social and economic impacts. To help improve our knowledge of the rainfall climate, we have constructed a 30-year (1983–2012), temporally consistent rainfall dataset for Africa known as TARCAT (TAMSAT African Rainfall Climatology And Time-series) using archived Meteosat thermal infra-red (TIR) imagery, calibrated against rain gauge records collated from numerous African agencies. TARCAT has been produced at 10-day (dekad) scale at a spatial resolution of 0.0375°. An intercomparison of TARCAT from 1983 to 2010 with six long-term precipitation datasets indicates that TARCAT replicates the spatial and seasonal rainfall patterns and interannual variability well, with correlation coefficients of 0.85 and 0.70 with the Climate Research Unit (CRU) and Global Precipitation Climatology Centre (GPCC) gridded-gauge analyses respectively in the interannual variability of the Africa-wide mean monthly rainfall. The design of the algorithm for drought monitoring leads to TARCAT underestimating the Africa-wide mean annual rainfall on average by −0.37 mm day−1 (21%) compared to other datasets. As the TARCAT rainfall estimates are historically calibrated across large climatically homogeneous regions, the data can provide users with robust estimates of climate related risk, even in regions where gauge records are inconsistent in time.
Article
Decision-relevant information on the past climate, recent trends, likely future trajectories, anomalies and associated impacts is a prerequisite for decision-making at different levels. Unfortunately, climate information is often not available or, where it does exist, is inaccessible to those that need it most. The Enhancing National Climate Services (ENACTS) initiative is an ambitious effort to simultaneously improve the availability, access and use of climate information. This is accomplished by working with National Meteorological and Hydrological Services (NMHS) in Africa to develop high-resolution, spatially and temporally complete gridded historical meteorological datasets; produce suites of derived climate information products; and disseminate them through a web-based platform. ENACTS enables the NMHS to provide enhanced services by overcoming the challenges of data quality, availability and access – while at the same time fostering stakeholder engagement and use. The new data products allow for characterization of climate risks at a local scale and offers opportunities to support applications and research. ENACTS has so far been implemented in 10 countries at the national level and at regional levels in East Africa and the West African Sahel. This paper provides an outline of challenges and opportunities in ENACTS implementation, and the potential for scaling up across Africa.