Satellite rainfall climatology: a review

Kidd, Chris

doi:10.1002/joc.635

Cited by 173 publications

(92 citation statements)

References 60 publications

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“…Satellite-based rainfall estimates have been seen for some time as a way to provide rainfall data at the required temporal and spatial scales (Kidd, 2001). …”

Section: Introductionmentioning

confidence: 99%

The TAMORA algorithm: satellite rainfall estimates over West Africa using multi-spectral SEVIRI data

et al. 2010

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Abstract.A multi-spectral rainfall estimation algorithm has been developed for the Sahel region of West Africa with the purpose of producing accumulated rainfall estimates for drought monitoring and food security. Radar data were used to calibrate multi-channel SEVIRI data from MSG, and a probability of rainfall at several different rain-rates was established for each combination of SEVIRI radiances. Radar calibrations from both Europe (the SatPrecip algorithm) and Niger (TAMORA algorithm) were used. 10 day estimates were accumulated from SatPrecip and TAMORA and compared with kriged gauge data and TAMSAT satellite rainfall estimates over West Africa. SatPrecip was found to produce large overestimates for the region, probably because of its non-local calibration. TAMORA was negatively biased for areas of West Africa with relatively high rainfall, but its skill was comparable to TAMSAT for the low-rainfall region climatologically similar to its calibration area around Niamey. These results confirm the high importance of local calibration for satellite-derived rainfall estimates. As TAMORA shows no improvement in skill over TAMSAT for dekadal estimates, the extra cloud-microphysical information provided by multi-spectral data may not be useful in determining rainfall accumulations at a ten day timescale. Work is ongoing to determine whether it shows improved accuracy at shorter timescales.

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“…Satellite-based rainfall estimates have been seen for some time as a way to provide rainfall data at the required temporal and spatial scales (Kidd, 2001). …”

Section: Introductionmentioning

confidence: 99%

The TAMORA algorithm: satellite rainfall estimates over West Africa using multi-spectral SEVIRI data

et al. 2010

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“…Review papers on the determination of rainfall from satellite measurements have been prepared, by, for instance, Barrett (1988), Barrett and Beaumont (1994), Petty (1995), Petty and Krajewski (1996), Kummerow et al (1996), , Kidd (2001), Stephens and Kummerow (2007), and Huffman et al (2007). A selection of available rainfall products based on remote sensing techniques -sometimes used in combination with other methodologies -is presented in Table 1.…”

Section: Rainfallmentioning

confidence: 99%

Spatial evapotranspiration, rainfall and land use data in water accounting – Part 1: Review of the accuracy of the remote sensing data

Karimi

Bastiaanssen

2015

Hydrol. Earth Syst. Sci.

124

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Abstract. The scarcity of water encourages scientists to develop new analytical tools to enhance water resource management. Water accounting and distributed hydrological models are examples of such tools. Water accounting needs accurate input data for adequate descriptions of water distribution and water depletion in river basins. Ground-based observatories are decreasing, and not generally accessible. Remote sensing data is a suitable alternative to measure the required input variables. This paper reviews the reliability of remote sensing algorithms to accurately determine the spatial distribution of actual evapotranspiration, rainfall and land use. For our validation we used only those papers that covered study periods of seasonal to annual cycles because the accumulated water balance is the primary concern. Review papers covering shorter periods only (days, weeks) were not included in our review. Our review shows that by using remote sensing, the absolute values of evapotranspiration can be estimated with an overall accuracy of 95 % (SD 5 %) and rainfall with an overall absolute accuracy of 82 % (SD 15 %). Land use can be identified with an overall accuracy of 85 % (SD 7 %). Hence, more scientific work is needed to improve the spatial mapping of rainfall and land use using multiple space-borne sensors. While not always perfect at all spatial and temporal scales, seasonally accumulated actual evapotranspiration maps can be used with confidence in water accounting and hydrological modeling.

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“…Since the first satellite-derived precipitation observations were made in the 1970s [6], various hydrological and meteorological satellites have provided large quantities of valuable weather and climate data. A number of techniques have been developed to estimate precipitation from visible and/or infrared satellite observations based on the concept that cold/bright clouds are related to convection and are likely to produce rain.…”

Section: Introductionmentioning

confidence: 99%

Effects of 4D-Var Data Assimilation Using Remote Sensing Precipitation Products in a WRF Model over the Complex Terrain of an Arid Region River Basin

Pan

Cheng

et al. 2017

Remote Sensing

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Individually, ground-based, in situ observations, remote sensing, and regional climate modeling cannot provide the high-quality precipitation data required for hydrological prediction, especially over complex terrains. Data assimilation techniques can be used to bridge the gap between observations and models by assimilating ground observations and remote sensing products into models to improve precipitation simulation and forecasting. However, only a small portion of satellite-retrieved precipitation products assimilation research has been implemented over complex terrains in an arid region. Here, we used the weather research and forecasting (WRF) model to assimilate two satellite precipitation products (The Tropical Rainfall Measuring Mission: TRMM 3B42 and Fengyun-2D: FY-2D) using the 4D-Var data assimilation method for a typical inland river basin in northwest China's arid region, the Heihe River Basin, where terrains are very complex. The results show that the assimilation of remote sensing precipitation products can improve the initial WRF fields of humidity and temperature, thereby improving precipitation forecasting and decreasing the spin-up time. Hence, assimilating TRMM and FY-2D remote sensing precipitation products using WRF 4D-Var can be viewed as a positive step toward improving the accuracy and lead time of numerical weather prediction models, particularly over regions with complex terrains.

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Satellite rainfall climatology: a review

Cited by 173 publications

References 60 publications

The TAMORA algorithm: satellite rainfall estimates over West Africa using multi-spectral SEVIRI data

The TAMORA algorithm: satellite rainfall estimates over West Africa using multi-spectral SEVIRI data

Spatial evapotranspiration, rainfall and land use data in water accounting – Part 1: Review of the accuracy of the remote sensing data

Effects of 4D-Var Data Assimilation Using Remote Sensing Precipitation Products in a WRF Model over the Complex Terrain of an Arid Region River Basin

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