Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution
Kilibarda, M. ; Hengl, T. ; Heuvelink, G.B.M. ; Graler, B. ; Pebesma, E. ; Tadic, M.P. ; Bajat, B. - \ 2014
Journal of Geophysical Research: Atmospheres 119 (2014)5. - ISSN 2169-897X - p. 2294 - 2313.
daily climate extremes - space-time climate - data set - spatial interpolation - surface temperature - daily precipitation - air-temperature - part ii - variability - geostatistics
Combined Global Surface Summary of Day and European Climate Assessment and Dataset daily meteorological data sets (around 9000 stations) were used to build spatio-temporal geostatistical models and predict daily air temperature at ground resolution of 1km for the global land mass. Predictions in space and time were made for the mean, maximum, and minimum temperatures using spatio-temporal regression-kriging with a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) 8 day images, topographic layers (digital elevation model and topographic wetness index), and a geometric temperature trend as covariates. The accuracy of predicting daily temperatures was assessed using leave-one-out cross validation. To account for geographical point clustering of station data and get a more representative cross-validation accuracy, predicted values were aggregated to blocks of land of size 500x500km. Results show that the average accuracy for predicting mean, maximum, and minimum daily temperatures is root-mean-square error (RMSE) =2 degrees C for areas densely covered with stations and between 2 degrees C and 4 degrees C for areas with lower station density. The lowest prediction accuracy was observed at high altitudes (>1000m) and in Antarctica with an RMSE around 6 degrees C. The model and predictions were built for the year 2011 only, but the same methodology could be extended for the whole range of the MODIS land surface temperature images (2001 to today), i.e., to produce global archives of daily temperatures (a next-generation repository) and to feed various global environmental models. Key Points Global spatio-temporal regression-kriging daily temperature interpolation Fitting of global spatio-temporal models for the mean, maximum, and minimum temperatures Time series of MODIS 8 day images as explanatory variables in regression part
Climatology of daily rainfall semi-variance in The Netherlands
Beek, C.Z. van de; Leijnse, H. ; Torfs, P.J.J.F. ; Uijlenhoet, R. - \ 2011
Hydrology and Earth System Sciences 15 (2011)1. - ISSN 1027-5606 - p. 171 - 183.
spatial variability - daily precipitation - gauge measurements - extreme rainfall - united-states - radar - calibration - resolution - hydrology - sensitivity
Rain gauges can offer high quality rainfall measurements at their locations. Networks of rain gauges can offer better insight into the space-time variability of rainfall, but they tend to be too widely spaced for accurate estimates between points. While remote sensing systems, such as radars and networks of microwave links, can offer good insight in the spatial variability of rainfall they tend to have more problems in identifying the correct rain amounts at the ground. A way to estimate the variability of rainfall between gauge points is to interpolate between them using fitted variograms. If a dense rain gauge network is lacking it is difficult to estimate variograms accurately. In this paper a 30-year dataset of daily rain accumulations gathered at 29 automatic weather stations operated by KNMI (Royal Netherlands Meteorological Institute) and a one-year dataset of 10 gauges in a network with a radius of 5 km around CESAR (Cabauw Experimental Site for Atmospheric Research) are employed to estimate variograms. Fitted variogram parameters are shown to vary according to season, following simple cosine functions. Semi-variances at short ranges during winter and spring tend to be underestimated, but semi-variances during summer and autumn are well predicted.
Using ERA-INTERIM for regional crop yield forecasting in Europe
Wit, A.J.W. de; Baruth, B. ; Boogaard, H.L. ; Diepen, C.A. van; Kraalingen, D.W.G. van; Micale, F. ; Roller, J.A. te; Supit, I. ; Wijngaart, R. van der - \ 2010
Climate Research 44 (2010)1. - ISSN 0936-577X - p. 41 - 53.
daily precipitation - models - simulation - variability - radiation
Agrometeorological systems for regional crop yield forecasting have traditionally relied on weather data derived from weather stations for crop simulation and yield prediction. In recent years, numerical weather prediction (NWP) models have become an interesting source of weather data with the potential to replace observed weather data. This is a result of the steadily decreasing NWP grid sizes and the availability of long and consistent time-series through the so-called reanalysis projects. We evaluated the ERA-INTERIM reanalysis data set from the European Centre for Medium-range Weather Forecasting for regional crop yield forecasting. Crop simulations were carried out using 2 identical model implementations: one using interpolated observed weather, the other using weather data derived from ERA-INTERIM. Output for both sources of weather variables was generated for the EU27 and neighbouring countries and 14 crops, aggregated to national level and validated using reported crop yields from the European Statistical Office. The results indicate that the system performs very similar in terms of crop yield forecasting skill for both sources of weather variables. In 38% of the crop–country combinations, the forecasting error can be reduced by more than 10% of the baseline forecast (the trend only) for both sources of weather variables. In almost 20% of the crop–country combinations, the forecasting error can be reduced by more than 25% of the baseline forecast. The results demonstrate that the ERA-INTERIM data set is highly suitable for regional crop yield forecasting over Europe and may be used for implementing regional crop forecasting over data-sparse regions. Finally, we conclude that there is a need to improve the crop calendar and/or calibration for some of the modelled crops
Estimation of extreme floods of the River Meuse using a stochastic weather generator and a rainfall-runoff model
Leander, R. ; Buishand, A. ; Aalders, P. ; Wit, M. de - \ 2005
Hydrological Sciences Journal 50 (2005)6. - ISSN 0262-6667 - p. 1089 - 1103.
daily precipitation - time-series
A stochastic weather generator has been developed to simulate long daily sequences of areal rainfall and station temperature for the Belgian and French sub-basins of the River Meuse. The weather generator is based on the principle of nearest-neighbour resampling. In this method rainfall and temperature data are sampled simultaneously from multiple historical records with replacement such that the temporal and spatial correlations are well preserved. Particular emphasis is given to the use of a small number of long station records in the resampling algorithm. The distribution of the 10-day winter maxima of basin-average rainfall is quite well reproduced. The generated sequences were used as input for hydrological simulations with the semi-distributed HBV rainfall¿runoff model. Though this model is capable of reproducing the flood peaks of December 1993 and January 1995, it tends to underestimate the less extreme daily peak discharges. This underestimation does not show up in the 10-day average discharges. The hydrological simulations with the generated daily rainfall and temperature data reproduce the distribution of the winter maxima of the 10-day average discharges well. Resampling based on long station records leads to lower rainfall and discharge extremes than resampling from the data over a shorter period for which areal rainfall was available.
Simulation of 6-hourly rainfall and temperature by two resampling schemes
Wójcik, R. ; Buishand, T.A. - \ 2003
Journal of Hydrology 273 (2003). - ISSN 0022-1694 - p. 69 - 80.
neerslag - regen - luchttemperatuur - stochastische modellen - nederland - precipitation - rain - air temperature - stochastic models - netherlands - daily precipitation - rhine basin - model - series
The joint simulation of time series of 6-hourly precipitation and temperature using nearest-neighbour resampling is studied for Maastricht, the Netherlands. Two resampling schemes are considered: (i) straightforward resampling of 6-hourly values, and (ii) resampling of daily values followed by disaggregation into 6-hourly values using the method of fragments. Second-order statistics of the simulated values are compared with those in the observed data. It appeared that straightforward resampling of 6-hourly values does not adequately preserve the slow decay of the autocorrelation functions of precipitation and temperature. As a result the standard deviations of the monthly precipitation totals and monthly average temperature are strongly underestimated. A negative bias also shows up in the quantiles of the multi-day seasonal maximum precipitation amounts. The autocorrelation coefficients and the standard deviations of the monthly values are much better reproduced if the daily values are generated first. A good correspondence between the historical and simulated distributions of the seasonal maximum precipitation amounts is also achieved with this alternative resampling scheme. (C) 2003 Elsevier Science B.V. All rights reserved.