Evaluation of an improved daily solar radiation generator for the southeastern USA

García, Axel García y; Hoogenboom, Gerrit

doi:10.3354/cr029091

Cited by 34 publications

(8 citation statements)

References 21 publications

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“…Indeed, rainfall occurrences play a central role in various applications or models. For example, they are used as a conditioning variable in various stochastic models (i.e., its value is used as a covariate to condition stochastic models), such as for daily solar irradiance simulations [e.g., Hansen , ; Garcia y Garcia and Hoogenboom , ] or to condition some parameterizations, e.g., in process‐based snowmelt modelling where occurrence can be used as a surrogate for cloud cover [ Walter et al , ]. Consequently, a reliable simulation (or correction) of rainfall occurrence is therefore a prerequisite to drive and apply correctly subsequent models.…”

Section: Introductionmentioning

confidence: 99%

Bias correction of precipitation through Singularity Stochastic Removal: Because occurrences matter

2016

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This study focuses on how the treatment of the rainfall occurrences in bias correction (BC) contexts may affect the resulting precipitation, both in terms of occurrence and intensity. Three methodologies are compared-the "direct approach" (DA), the "Threshold Adaptation" approach (TA), and the "Positive Approach" (Pos)-as well as a method called "Singularity Stochastic Removal" (SSR) specifically developed for precipitation, all based on the same adjustment technique. Unlike the three other models, SSR allows dealing in the same way with the situations where the precipitation model has too many wet days or not with respect to the reference data. SSR also avoids separating the correction of the occurrence from that of the intensity, which constitutes a flexible tool. First, the four approaches are applied to a historical regional climate model precipitation run. Evaluations are realized through occurrence-and intensity-related criteria. Although SSR, DA, and Pos may be close to each other depending on the criterion, in general, SSR provides the best results when all criteria are accounted for. This is even more true when the classical assumption that "the model precipitation had too many wet days" does not hold. The BC methods are also intercompared over the 2071-2100 period. The different BC methods are in agreement with previous studies, with relatively equivalent evolutions from 1976-2005 to 2071-2100, although nuances are present from one BC method to another. As a global conclusion, the SSR method for precipitation is a good compromise to correct both occurrences and intensities.

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Section: Introductionmentioning

confidence: 99%

Bias correction of precipitation through Singularity Stochastic Removal: Because occurrences matter

2016

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“…For an efficient agriculture planning and climate risk assessment, a minimum quality of long-term weather data is required for design adequate strategies at regional and national spatial scales (Garcia and Hoogenboom, 2005).…”

Section: Introductionmentioning

confidence: 99%

Assessment of NASA/POWER satellite‐based weather system for Brazilian conditions and its impact on sugarcane yield simulation

Monteiro¹,

Sentelhas

Pedra³

2017

Intl Journal of Climatology

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The gridded database provided by National Aeronautics and Space Administration/Prediction of World Wide Energy Resources (NASA/POWER) presents a global coverage of complete weather data at horizontal resolution of 1° latitude–longitude, becoming a potential source for agrometeorological studies. Once Brazil is a country with continental dimensions and the major sugarcane world producer, and its density of ground weather stations suitable for an efficient agricultural planning is sparse, the objectives of this study were to test how robust is the NASA/POWER database through its comparison with the Brazilian ground weather stations network records (INMET) and to quantify the impacts on potential (Yp) and attainable (Yatt) sugarcane yield simulations when setting NASA/POWER as source of input weather data. The comparisons for weather data records and sugarcane yield simulations were carried out from 1997 to 2016. Statistical indices presented a satisfactory performance for average air temperature (R2 = 0.73; d = 0.91), minimum air temperature (R2 = 0.72; d = 0.91), maximum air temperature (R2 = 0.57; d = 0.84), solar radiation (SR) (R2 = 0.71; d = 0.92), sunshine hours (R2 = 0.68; d = 0.90) and reference evapotranspiration, when calculated through Priestley–Taylor (ETo‐PT) method (R2 = 0.76; d = 0.93). When the weather variables were aggregated and compared with a 10‐day time scale, a strong improvement of statistical indices was obtained. Yp presented root mean square error (RMSE) smaller than 10 t ha−1 while relative mean error (RME) ranged between ±10% for majority of grid cells, with exception for southern Brazil due to low and frost temperatures that satellite cannot capture accurately. Even NASA/POWER offering a relatively coarse grid size database and perhaps some regional data fitting would give better results at higher latitudes and elevation. The results found in this study proved that NASA/POWER products could be used as a source of climatic data for agricultural activities with a reasonable confidence for regional and national spatial scales.

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“…The daily solar radiation, maximum and minimum air temperature, and precipitation for 163 each location were obtained from the Georgia Automated Environmental Monitoring Network 164 (GAEMN, www.georgiaweather.net), which was first deployed in 1991 (Hoogenboom, 165 1996),with 60 operational stations in 2004 (Garcia y Garcia and Hoogenboom, 2005) Crop management, planting dates, irrigation amount, fertilizer amount, and planting 175 population corresponded to the local management of the variety trials. Plant population at seeding 176 was around 6 to 8 plants/m 2 , row spacing was 76 cm, and the planting depth was 5 cm.…”

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confidence: 99%

A comparison of the performance of the CSM-CERES-Maize and EPIC models using maize variety trial data

Bao

Hoogenboom

McClendon

et al. 2017

Agricultural Systems

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Evaluation of an improved daily solar radiation generator for the southeastern USA

Cited by 34 publications

References 21 publications

Bias correction of precipitation through Singularity Stochastic Removal: Because occurrences matter

Bias correction of precipitation through Singularity Stochastic Removal: Because occurrences matter

Assessment of NASA/POWER satellite‐based weather system for Brazilian conditions and its impact on sugarcane yield simulation

A comparison of the performance of the CSM-CERES-Maize and EPIC models using maize variety trial data

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