Rainfall and temperature estimation for a data sparse region

Wilby, Robert L.; Yu, Dapeng

doi:10.5194/hess-17-3937-2013

Cited by 33 publications

(17 citation statements)

References 29 publications

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“…• Several diagnostics were derived noting that, because there are only five years of simulations, extreme rainfall can not be assessed. Instead, we have characterized how well WRF and SDSM simulate the following widely used indices (Haylock et al 2006;Wilby and Yu 2013;Nicholls and Murray 1999):…”

Section: Analytical Methods and Diagnostics Of Model Skillmentioning

confidence: 99%

Harmonized evaluation of daily precipitation downscaled using SDSM and WRF+WRFDA models over the Iberian Peninsula

et al. 2019

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There have been numerous statistical and dynamical downscaling model comparisons. However, differences in model skill can be distorted by inconsistencies in experimental set-up, inputs and output format. This paper harmonizes such factors when evaluating daily precipitation downscaled over the Iberian Peninsula by the Statistical DownScaling Model (SDSM) and two configurations of the dynamical Weather Research and Forecasting Model (WRF) (one with data assimilation (D) and one without (N)). The ERA-Interim reanalysis at 0.75 • resolution provides common inputs for spinning-up and driving the WRF model and calibrating SDSM. WRF runs and SDSM output were evaluated against ECA&D stations, TRMM, GPCP and EOBS gridded precipitation for 2010-2014 using the same suite of diagnostics. Differences between WRF and SDSM are comparable to observational uncertainty, but the relative skill of the downscaling techniques varies with diagnostic. The SDSM ensemble mean, WRF-D and ERAI have similar correlation scores ( r = 0.45-0.7), but there were large variations amongst SDSM ensemble members ( r = 0.3-0.6). The best Linear Error in Probability Space ( LEPS = 0.001-0.007) and simulations of precipitation amount were achieved by individual members of the SDSM ensemble. However, the Brier Skill Score shows these members do not improve the prediction by ERA-Interim, whereas precipitation occurrence is reproduced best by WRF-D. Similar skill was achieved by SDSM when applied to station or gridded precipitation data. Given the greater computational demands of WRF compared with SDSM, clear statements of expected value-added are needed when applying the former to climate impacts and adaptation research.Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Section: Analytical Methods and Diagnostics Of Model Skillmentioning

confidence: 99%

Harmonized evaluation of daily precipitation downscaled using SDSM and WRF+WRFDA models over the Iberian Peninsula

et al. 2019

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“…The IA (Willmott, 1981) is another widely used indicator of goodness of fit between observed and model output. IA (Eq.…”

Section: Comparing Ground Data With Satellite Observational Reanalysmentioning

confidence: 99%

“…In order to understand the impacts of future climate at the regional and local scale, ground station data with high spatial and temporal resolution are crucial. Regions with poor ground observations are highly vulnerable to climate threats (Wilby and Yu, 2013), which holds particularly for developing countries. In Africa, high-quality climate data from meteorological field stations are scarce (Dinku et al, 2013) and inconsistencies exist between other data products, largely due to a limited number of ground stations, merging and interpolation methods (Huffman et al, 2009;Nikulin et al, 2012;Sylla et al, 2013), limited time resolution, and limited documentation quality.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of multiple climate data sources for managing environmental resources in East Africa

Gebrechorkos

Hülsmann

Bernhofer

2018

Hydrol. Earth Syst. Sci.

133

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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.

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“…Notably, studies in remote and data sparse areas, where most of the agricultural activities are happening, are limited. A study 22 concluded that regions with poor climate information are highly vulnerable to climate change and variability, which holds for East Africa. Identifying areas with a changing in climate (e.g., rainfall and temperature) requires spatial information with higher resolution, which could help better management of the impacts.…”

Section: Introductionmentioning

confidence: 99%

Long-term trends in rainfall and temperature using high-resolution climate datasets in East Africa

Gebrechorkos

Hülsmann

Bernhofer

2019

Sci Rep

171

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Detecting changes in climate is a prerequisite for a better understanding of the climate and developing adaptation and mitigation measures at a regional and local scale. In this study long-term trends in rainfall and maximum and minimum temperature (T-max and T-min) were analysed on seasonal and annual time scales for East Africa. High resolution gridded rainfall (1981–2016) and temperature (1979–2010) data from international databases like the Climate Hazards Group are used. Long-term seasonal trend analysis shows a non-significant (except for small areas), decreasing (increasing) trend in rainfall in eastern (western) parts of Ethiopia and Kenya and a decreasing trend in large parts of Tanzania during the long rainy season. On the other hand, a non-significant increasing trend in large parts of the region is observed during the short rain season. With regard to annual trends, results largely confirm seasonal analyses: only a few significant trends in rainfall, but significant increasing trends in T-max (up to 1.9 °C) and T-min (up to 1.2 °C) for virtually the whole region. Our results demonstrate the need and added value of analysing climate trends based on data with high spatial resolution allowing sustainable adaptation measures at local scales.

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Rainfall and temperature estimation for a data sparse region

Cited by 33 publications

References 29 publications

Harmonized evaluation of daily precipitation downscaled using SDSM and WRF+WRFDA models over the Iberian Peninsula

Harmonized evaluation of daily precipitation downscaled using SDSM and WRF+WRFDA models over the Iberian Peninsula

Evaluation of multiple climate data sources for managing environmental resources in East Africa

Long-term trends in rainfall and temperature using high-resolution climate datasets in East Africa

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