{'fraction_non_alphanumeric': 0.037728658536585365, 'fraction_numerical': 0.011432926829268292, 'mean_word_length': 5.25, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Evaluation of multiple climate data sources for managing environmental resources in East Africa

Abstract. Managing environmental resources under conditions of climate change and
extreme climate events remains among the most challenging research tasks in
the field of sustainable development. A particular challenge in many regions
such as East Africa is often the lack of sufficiently long-term and spatially
representative observed climate data. To overcome this data challenge we used
a combination of accessible data sources based on station data, earth
observations by remote sensing, and regional climate models. The accuracy of
the Africa Rainfall Climatology version 2.0 (ARC2), Climate Hazards Group
InfraRed Precipitation (CHIRP), CHIRP with Station data (CHIRPS),
Observational-Reanalysis Hybrid (ORH), and regional climate models (RCMs) are
evaluated against station data obtained from the respective national weather
services and international databases. We did so by performing a comparison in
three ways: point to pixel, point to area grid cell average, and stations'
average to area grid cell average over 21 regions of East Africa: 17 in
Ethiopia, 2 in Kenya, and 2 in Tanzania. We found that the latter method
provides better correlation and significantly reduces biases and errors. The
correlations were analysed at daily, dekadal (10 days), and monthly
resolution for rainfall and maximum and minimum temperature (Tmax
and Tmin) covering the period of 1983–2005. At a daily
timescale, CHIRPS, followed by ARC2 and CHIRP, is the best performing
rainfall product compared to ORH, individual RCMs (I-RCM), and RCMs' mean
(RCMs). CHIRPS captures the daily rainfall characteristics well, such as
average daily rainfall, amount of wet periods, and total rainfall. Compared
to CHIRPS, ARC2 showed higher underestimation of the total (−30 %) and
daily (−14 %) rainfall. CHIRP, on the other hand, showed higher
underestimation of the average daily rainfall (−53 %) and duration of
dry periods (−29 %). Overall, the evaluation revealed that in terms of
multiple statistical measures used on daily, dekadal, and monthly timescales,
CHIRPS, CHIRP, and ARC2 are the best performing rainfall products, while ORH,
I-RCM, and RCMs are the worst performing products. For Tmax and Tmin, ORH was identified as the most
suitable product compared to I-RCM and RCMs. Our results indicate that CHIRPS
(rainfall) and ORH (Tmax and Tmin), with higher
spatial resolution, should be the preferential data sources to be used for
climate change and hydrological studies in areas of East Africa where station
data are not accessible.
