Forest climatology: estimation and use of daily climatological data for Bavaria, Germany

Xia, Youlong; Fabian, P.; Winterhalter, Martin; Zhao, Ming

doi:10.1016/s0168-1923(00)00210-0

Cited by 42 publications

(28 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Methods for handling missing data with daily resolution, on the other hand, are scarce and show marked errors, even though such methods perform well at lower resolution time * Correspondence to: M. Brunetti, ISAC-CNR, Via P. Gobetti,101, scales (e.g. DeGaetano et al, 1995;Xia et al, 2001). The situation becomes particularly complicated when dealing with precipitation, because of its large space and time variability; moreover, in this case, the problem is twofold, since both time location and rainfall amount of each single-day event must be reconstructed.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improving estimation of missing values in daily precipitation series by a probability density function‐preserving approach

Simolo

Brunetti

Maugeri

et al. 2010

Intl Journal of Climatology

View full text Add to dashboard Cite

This work presents a novel method for estimating missing values in daily precipitation series. It is aimed at identifying the event time location with good accuracy and reconstructing the correct amount of daily rainfall. In addition, the statistical properties of the time series, i.e. both probability distribution and long-term statistics, are preserved. The completion method is based on a two-step algorithm that uses information from a cluster of neighboring stations. First, wet and dry days are tagged, and subsequently, the full precipitation amount for wet-classified days is estimated by a modified multi-linear regression approach. This method avoids overestimation of the number of wet days and underestimation of intense precipitation events, which are typical side effects of common regression-based approaches.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Furthermore, simplified schemes, including the so-called 'closest station method' and 'single best estimator' (e.g. Wallis et al, 1991;Eischeid et al, 2000;Xia et al, 2001), are also currently used for estimating missing values in precipitation series.…”

Section: Introductionmentioning

confidence: 99%

Improving estimation of missing values in daily precipitation series by a probability density function‐preserving approach

Simolo

Brunetti

Maugeri

et al. 2010

Intl Journal of Climatology

View full text Add to dashboard Cite

show abstract

“…These methods take into account the spatial variability, but they largely ignore the temporal information in long-time series. Some of the methods that follow this approach are the closest station (CS) method (Xia et al, 1999;Xia et al, 2001), the simple arithmetic averaging (SAA) method, the inverse distance method, the single best estimator (SBE) method, and the normal ratio (NR) method. The SAA method (Xia et al, 1999) fills in the missing value with the arithmetic mean of the synchronous values for five stations with similar characteristics and close to the target station.…”

Section: P Ramos-calzado Et Almentioning

confidence: 99%

A novel approach to precipitation series completion in climatological datasets: application to Andalusia

Ramos-Calzado

Gómez‐Camacho

Pérez‐Bernal

et al. 2008

Intl Journal of Climatology

View full text Add to dashboard Cite

ABSTRACT:Complete monthly series of precipitation data have been generated for 932 stations run by the Instituto Nacional de Meteorología (INM) in Andalusia (Andalucía, southern region of Spain) and some adjacent areas for the period 1961-2000 using a new method for the estimation of missing data. The proposed method fills in the gaps in the climatological series for a target station using precipitation data from neighbouring stations with a similar precipitation pattern. Particular emphasis is made in the consideration of the precipitation data uncertainty. A set of possible estimates for the missing data are obtained assuming a linear relationship between the target station and the selected neighbour stations. The final imputed value is the median of the previously evaluated estimates distribution. This approach also provides the uncertainty of the missing data estimate. As an application, the suggested approach is used to obtain a precipitation map of Andalusia, as well as the average annual series for the examined period.

show abstract

“…The widely-used patching methods include: closest station, simple arithmetic averaging, inverse distance weighting, multiple regression and normal ratio (Tang et al, 1996;Makhuvha et al, 1997;Xia et al, 1999;Xia et al, 2001). In utilizing the closest station method, the nearest weather station with data corresponding to the period of concern is identified and missing values are either replaced directly by the value at the neighbour station or adjusted by a factor from the ratio of long-term means between the two stations (Xia et al, 2001). In simple arithmetic averaging, the missing data are obtained by arithmetically averaging data of the 2 to 5 closest weather stations around a station (Tang et al, 1996;Xia et al, 1999;De Silva et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of an inverse distance weighting method for patching daily and dekadal rainfall over the Free State Province, South Africa

Moeletsi¹,

Shabalala²,

Nysschen³

et al. 2016

WSA

View full text Add to dashboard Cite

Climate data recorded by national meteorological agencies is either incomplete or faulty for some periods due to a number of reasons. Multi-functional utilization of climate data in complete form necessitates the filling of these gaps. In this study an inverse distance weighting (IDW) method was used to estimate rainfall utilizing neighbouring station data in the Free State Province of South Africa. Six weather stations evenly distributed across the province, and with data for 1950 to 2008, were used to evaluate this patching IDW approach at daily and dekadal time steps. Coefficient of determination (r 2 ), mean absolute error (MAE) and mean bias error (MBE) were the statistics used in the assessment. Firstly, the study conducted a sensitivity analysis of the IDW exponent (p) which showed that the best results are obtained when p is either 2 or 2.5. The estimated values at all six stations were highly correlated with the measured rainfall data with an overall r 2 value exceeding 0.70 for both daily and dekadal estimates. MAE showed low miscalculations with values with an average of 1 mm per day and 4.4 mm per dekad. MBE was very low for both daily and dekadal evaluations but the disaggregated data showed underestimation of the IDW mostly for daily rainfall exceeding 10 mm. Thus, IDW methodology proved to be an acceptable approach for estimating both daily and dekadal rainfall in the Free State Province.

show abstract

Forest climatology: estimation and use of daily climatological data for Bavaria, Germany

Cited by 42 publications

References 35 publications

Improving estimation of missing values in daily precipitation series by a probability density function‐preserving approach

Improving estimation of missing values in daily precipitation series by a probability density function‐preserving approach

A novel approach to precipitation series completion in climatological datasets: application to Andalusia

Evaluation of an inverse distance weighting method for patching daily and dekadal rainfall over the Free State Province, South Africa

Contact Info

Product

Resources

About