Spatial interpolation of meteorological data and forecasting rainfall using ensemble techniques

Introduction: Evapotranspiration is key in the management of arid agricultural areas. In Chihuahua, the volume of irrigation water is based on reference evapotranspiration (ETo) calculated with empirical methods and extrapolated to the cropped area, which is inaccurate. The alternative is to calculate ETo variation by spatial interpolation. Objective: To analyze the spatio-temporal variation of ETo using empirical methods and spatial interpolation in Chihuahua, Mexico. Methodology: Records from 33 meteorological stations from 1960-2013 and seven ETo estimation methods were used. The results were compared with the Penman-Monteith method, modified by FAO (PMMF), ANOVA analysis (P ≤ 0.05), and homogeneous ETo surfaces built from the point values by spatial interpolation. Results: The Hargreaves method (R2 = 0.91, RMSE = 1.16 and ME = -0.69 mm-day-1) had a smaller bias with respect to PMMF. ETo values ranged from 2.5 to 7.1 mm-day-1 in a west-east direction, with maximum values at low elevations and minimum values at high elevations, which showed the influence of the Sierra Madre Occidental on ETo. This characteristic was most noticeable in the warm months (June to September). Limitations of the study: The use of estimated data needs field validation. Originality: The ETo estimation with seven empirical methods and one spatial interpolation method to extrapolate values to areas with scarce meteorological data. Conclusions: The Hargreaves method allows estimating the spatio-temporal variation of ETo in large extensions and areas with limited meteorological information.

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The use of serially complete station data to improve the temporal continuity of gridded precipitation and temperature estimates

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0313.1 ◽

2021 ◽

Author(s):

Guoqiang Tang ◽

Martyn P. Clark ◽

Simon Michael Papalexiou

Keyword(s):

North America ◽

Spatial Interpolation ◽

Missing Values ◽

Meteorological Data ◽

Maximum Temperature ◽

Spatial Density ◽

Gap Filling ◽

Station Network ◽

Interpolation Methods ◽

Precipitation And Temperature

AbstractStations are an important source of meteorological data, but often suffer from missing values and short observation periods. Gap filling is widely used to generate serially complete datasets (SCDs), which are subsequently used to produce gridded meteorological estimates. However, the value of SCDs in spatial interpolation is scarcely studied. Based on our recent efforts to develop a SCD over North America (SCDNA), we explore the extent to which gap filling improves gridded precipitation and temperature estimates. We address two specific questions: (1) Can SCDNA improve the statistical accuracy of gridded estimates in North America? (2) Can SCDNA improve estimates of trends on gridded data? In addressing these questions, we also evaluate the extent to which results depend on the spatial density of the station network and the spatial interpolation methods used. Results show that the improvement in statistical interpolation due to gap filling is more obvious for precipitation, followed by minimum temperature and maximum temperature. The improvement is larger when the station network is sparse and when simpler interpolation methods are used. SCDs can also notably reduce the uncertainties in spatial interpolation. Our evaluation across North America from 1979 to 2018 demonstrates that SCDs improve the accuracy of interpolated estimates for most stations and days. SCDNA-based interpolation also obtains better trend estimation than observation-based interpolation. This occurs because stations used for interpolation could change during a specific period, causing changepoints in interpolated temperature estimates and affect the long-term trends of observation-based interpolation, which can be avoided using SCDNA. Overall, SCDs improve the performance of gridded precipitation and temperature estimates.

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