A new, long-term daily satellite-based rainfall dataset for operational monitoring in Africa

Maidment, Ross; Grimes, D. I. F.; Black, Emily; Tarnavsky, Elena; Young, Matthew; Greatrex, Helen; Allan, Richard P.; Stein, Thorwald H. M.; Nkonde, Edson; Senkunda, Samuel; Uribe-Alcántara, Edgar Misael

doi:10.1038/sdata.2017.63

Cited by 180 publications

(192 citation statements)

References 59 publications

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“…The works of Toté et al (2015) in Mozambique can be mentioned here as the first validation work we are aware of that reveals the potential applications of CHIRPS in Africa. Maidment et al (2017) have also validated the performance of satellite rainfall products (including CHIRPS V 2.0) in four countries in Africa (Mozambique, Nigeria, Uganda, and Zambia). In general, these few validation works have shown the promising skills of CHIRPS in Africa and its potentials for various working applications in the continent.…”

Section: Introductionmentioning

confidence: 99%

“…In the course of this analysis, both the TAMSAT and ARC 2 products have been validated as well. Also, this study has also been paid some courtesy to compare TAMSAT 2 and TAMSAT 3 products to assess the improvements made with the new version (TAMSAT 3, Maidment et al, 2017). The analyses used dekadal (10 days) and monthly time scale rainfall data both from the satellite products and rain 25 gauge observations.…”

Section: Introductionmentioning

confidence: 99%

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Validation of new satellite rainfall products over the Upper Blue Nile Basin, Ethiopia

Ayehu¹,

Tadesse²,

Gessesse³

et al. 2017

Preprint

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Abstract. Accurate measurement of rainfall is vital to analyze the spatial and temporal patterns of precipitation at variousscales. However, the conventional rain gauge observations in many parts of the world such as Ethiopia are sparse and unevenly distributed. An alternative to traditional rain gauge observations could be satellite-based rainfall estimates. Satellite 15 rainfall estimates could be used as a sole product (e.g. in areas with no (poor) ground observations) or through integrating with rain gauge measurements. In this study, the newly available Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data has been evaluated in comparison to rain gauge data for the period of 2000 to 2015 across the Upper Blue Nile basin in Ethiopia. Besides, the Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT) version 2 and 3 (TAMSAT 2 and TAMSAT 3) and the African Rainfall Climatology (ARC 2) products have 20 been used as a benchmark and compared with CHIRPS. The TAMSAT 2 rainfall estimate was used in this study mainly to assess the improvements made with the recent version of a TAMSAT product (TAMSAT 3). From the overall analysis at dekadal and monthly temporal scale, CHIRPS exhibited higher skills and the best bias value in comparison to ARC 2 but overestimates the frequency of rainfall occurrence particularly during the dry months. On the other hand, TAMSAT 3 has shown very comparable performance with that of CHIRPS product, particularly with regards to bias. The ARC 2 product 25 was found to have the weakest performance underestimating rainfall amounts by about 24%. The skill of CHIRPS is less affected by variation in elevation in comparison to TAMSAT 3 and ARC 2 products. While ARC 2 was found to be affected by elevation with the average biases of 1.53, 0.86 and 0.77 at lower (< 1000 m a.s.l), medium (1000 to 2000 m a.s.l) and higher elevation (> 2000 m a.s.l), respectively. Comparing the overall performance of the three products, CHIRPS exhibited the best performance followed closely by TAMSAT 3. This validation study also shows that the TAMSAT 3 has overcome 30 the main weaknesses of TAMSAT 2, which is underestimation of high rainfall amounts by up to 31% in this study. Overall, the finding of this validation study shows the potentials of CHIRPS product to be used for various operational applications such as rainfall pattern and variability study in the Upper Blue Nile basin in Ethiopia.Atmos. Meas. Tech. Discuss., https://doi

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Validation of new satellite rainfall products over the Upper Blue Nile Basin, Ethiopia

Ayehu¹,

Tadesse²,

Gessesse³

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…The rainfall estimates are derived from Meteosat thermal infrared images calibrated against an extensive network of African rain gauges. When aggregated over time and space, TAMSAT has been shown to have good skill over much of Africa, in comparison to ground-based observations (Maidment et al, 2013(Maidment et al, , 2017. On daily time scales, occurrence is better represented than amount (Greatrex et al, 2014), with the magnitude of high intensity rainfall events not captured.…”

Section: Tamsat Rainfall Observationsmentioning

confidence: 99%

“…The difference between JULES model outputs when forced with these two distinct products will help us to understand the impact of improved precipitation forcing on our estimation of soil moisture. It has been shown that TAMSAT v3.0 has greatly reduced the dry bias present in TAMSAT v2.0 (Maidment et al, 2017) and has eliminated the spatial artefacts. Despite this there are still areas 5 where both products struggle, with coastal regions subject to large amounts of warm rain, and sharp topographic contrasts, being an example of this.…”

Section: Tamsat Rainfall Observationsmentioning

confidence: 99%

Using satellite observations of precipitation and soil moisture to constrain the water budget of a land surface model

Pinnington

Quaife

Black

2017

Preprint

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Abstract. Early warning of agricultural drought can enable decision makers to act to improve food security. Land-surface models are useful tools to inform such monitoring systems, but model errors are problematic. We show that satellite-derived estimates of shallow soil moisture can be used to calibrate a land-surface model at the regional scale in Ghana, using data assimilation techniques. The modified calibration significantly improves model estimation of soil moisture. Specifically, we find a 44% reduction in root-mean-squared error for a 5-year hindcast after assimilating a single year of soil moisture ob-5 servations to update model parameters. The use of an improved remotely-sensed rainfall dataset contributes to 10% of this reduction in error. Improved rainfall data has the greatest impact on model estimates during the seasonal wetting-up of soil, with the assimilation of remotely sensed soil moisture having greatest impact during drying down. The significant reduction in root-mean-squared error we find after assimilating a single year of observations bodes well for the production of improved soil moisture forecasts over sub-Saharan Africa where subsistence farming remains prevalent.

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“…In this context, precipitation retrievals from a satellite provide an alternative solution to this problem by providing spatially distributed precipitation estimation over large areas. Various aspects of remotely-sensed rainfall have been explored and reported [16][17][18][19][20]. Among them, Tropical Rainfall Measuring Mission (TRMM) multisatellite precipitation analysis (TMPA) has been used, since the launch of TRMM in November 1997, to produce a range of quasi-global precipitation products.…”

Section: Introductionmentioning

confidence: 99%

Improving Rainfall Erosivity Estimates Using Merged TRMM and Gauge Data

Teng

Chappell

et al. 2017

Remote Sensing

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Soil erosion is a global issue that threatens food security and causes environmental degradation. Management of water erosion requires accurate estimates of the spatial and temporal variations in the erosive power of rainfall (erosivity). Rainfall erosivity can be estimated from rain gauge stations and satellites. However, the time series rainfall data that has a high temporal resolution are often unavailable in many areas of the world. Satellite remote sensing allows provision of the continuous gridded estimates of rainfall, yet it is generally characterized by significant bias. Here we present a methodology that merges daily rain gauge measurements and the Tropical Rainfall Measuring Mission (TRMM) 3B42 data using collocated cokriging (ColCOK) to quantify the spatial distribution of rainfall and thereby to estimate rainfall erosivity across China. This study also used block kriging (BK) and TRMM to estimate rainfall and rainfall erosivity. The methodologies are evaluated based on the individual rain gauge stations. The results from the present study generally indicate that the ColCOK technique, in combination with TRMM and gauge data, provides merged rainfall fields with good agreement with rain gauges and with the best accuracy with rainfall erosivity estimates, when compared with BK gauges and TRMM alone.

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A new, long-term daily satellite-based rainfall dataset for operational monitoring in Africa

Cited by 180 publications

References 59 publications

Validation of new satellite rainfall products over the Upper Blue Nile Basin, Ethiopia

Validation of new satellite rainfall products over the Upper Blue Nile Basin, Ethiopia

Using satellite observations of precipitation and soil moisture to constrain the water budget of a land surface model

Improving Rainfall Erosivity Estimates Using Merged TRMM and Gauge Data

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