Reproduction of precipitation characteristics. by interpolated weather generator. M. Dubrovsky (1), D. Semeradova (2), L. Metelka (3), M.

Transcription

1 Reproduction of precipitation characteristics by interpolated weather generator M. Dubrovsky (1), D. Semeradova (2), L. Metelka (3), M. Trnka (2) (1) Institute of Atmospheric Physics ASCR, Prague, Czechia (2) Institute of Landscape Ecology MAFU, Brno, Czechia (3) Czech Hydrometeorological Institute, Hradec Kralove, Czechia Acknowledgement: The contribution is an outcome of the CaliM&Ro project supported by the Grant Agency of the Czech Republic (project 205/05/2265).

2 Abstract Stochastic weather generators (WGs) are used to produce observed-like synthetic weather series, which may serve as an input to various weather-dependent models (e.g. crop models [1,2,3,4,5] and rainfall-runoff models [3]). Typically, WGs are used in assessing sensitivity of the modelled processes to variability and changes in climatic characteristics. CaliM&Ro project is focused on calibration of a stochastic single-site 4-variate daily weather generator Met&Roll [1,2,3,4,5,6] for sites with non-existing or incomplete historical daily weather series. Met&Roll is a parametric weather generator. It uses Markov chain (order = 1 to 3) to model precipitation occurrence, Gamma distribution to model precipitation amount and first-order 3-variate autoregressive model to model solar radiation and daily extreme temperatures. Daily generator may be optionally conditioned on the monthly generator [3] based on the 1st order autoregressive model. Parameters of the generator are allowed to exhibit annual cycle. WG parameters are interpolated from the surrounding stations. Here, we focus on parameters of the precipitation sub-model: parameters of the Gamma distribution and Markov chain model. The parameters are interpolated using simple co-kriging (using ArcGIS), and two regression-based interpolation techniques (neural networks and nearest neighbours). In all cases, effect of altitude is taken into account. The tests are based on observational data from 125 stations in Czechia. Of these, 100 stations are used for interpolation and 25 for validation. The performance of interpolation techniques is assessed in terms of (i) accuracy of interpolation of individual WG parameters (using RMSE and RV measures), (ii) accurracy of reproducing selected precipitation characteristics (annual precipitation extremes and duration characteristics of wet and dry spells) by interpolated generator. Gridded maps of selected WG parameters are constructed for Czechia: the parameters are interpolated into grid using the global GTOPO30 digital elevation model [

3 Met&Roll weather generator Met&Roll is a single-site 4-variate stochastic daily weather generator. variables: - precipitation amount (PREC) - daily temperature maximum (TMAX) - daily temperature minimum (TMIN) - global solar radiation (SRAD) model: - precipitation occurrence ~ Markov chain of 1 st to 3 rd order (parameters: transition probabilities) - precipitation amount ~ Gamma distribution (parameters: α, β) - (SRAD, TMAX, TMIN) ~ 3-variate 1st order AR model (parameters: means and std s /separately for wet and dry days/ of the three variables; two 3 3 matrices representing lag-0 and lag-1 covariances among the 3 variables) - daily weather generator is optionally (not applied in the present experiment!) linked to monthly generator (model: 1 st order AR; parameters: two 4 4 matrices) - most parameters of the generator follow annual cycle Met&Roll has been mainly used in agricultural studies [see references] dealing with sensitivity of crop yields to changes in climatic characteristics, including impact of the future climate change. It is also implemented in the PERUN system designed for climate change impact studies and probabilistic crop yields forecasting. Within the present project, a new system (M&Rwin) is being developed. This system links Met&Roll with crop models (now available are WOFOST and CERES models) and hydrological model Sacramento. Climate change scenarios and interpolation of WG parameters is implemented in this system, too.

4 GIS GIS is a system designed to capture, store, update, manipulate, analyze, and display the geographic information. ArcGIS is an integrated collection of GIS software products for handling spatial data, developed by Environmental Systems Research Institute (ESRI). For interpolation of selected WG parameters the Geostatistical Analyst (GA) extension of the version ArcView 9.1. were used, which provides a set of tools to create a continuous surface using deterministic and geostatistical methods. For this study geostatistical techniques (Kriging, Cokriging) based on both statistical and mathematical methods were selected. Kriging methods rely on the notion of autocorrelation as a function of distance. Predicted value depends on two factors: a trend and a fluctuation from the trend, called spatially-autocorrelated random error. Cokriging improves surface prediction of a primary variable by taking into account secondary variables, provided that the primary and secondary variables are spatially correlated. In first step the spatial data variability, spatial data dependence, and global trends were explored using Exploratory spatial data analysis tools included with GA. This phase helps to select an appropriate model to create a surface. In the next step diagnostics were performed to assess the uncertainty of the predictions. Subsequently various analytical tools and calculated statistics allow to compare models if more than one surface is produced.

5 Neural Networks Neural networks (NNs) are inspired by the signal processing in the biological neural systems. Artificial NN is composed from a network of individual neurons. Each neuron receives a number of inputs (either from original data, or from the output of other neurons. Each input comes via a connection (synapse) that has a strength (or weight, which corresponds to synaptic efficacy in a biological neuron) and a single threshold value. The weighted sum of the inputs minus threshold composes the activation of the neuron (= postsynaptic potential of the neuron). The activation signal is then passed through a non-linear activation function (= transfer function) to produce the output of the neuron. A simple network has a feedforward structure: signals flow from inputs (which carry the signal of independent variables), forwards through hidden units, eventually reaching the output units (which form predictions or dependent variables values). Quasi-optimal values of network free parameters (synaptic weights, neuron thresholds) are fixed during iterative training from randomly generated initial state of the network, several training algorithms are available. Trained neural network should mirror general features of training data (relations between independent and dependent variables, classification, etc.) and may be used for processing new cases. The maps shown here were made using the ensemble generalization: The network was repeatedly (99-times) trained with different network initialization and different data division into training and selection subsets. Final interpolated values are the mean values of all 99 individual models (ensemble members). [Multilayer Perceptron network type = 3-5-1, 29 degrees of freedom, Back Error Propagation and Conjugate Gradient Descent training algorithms used] [StatSoft, Inc. (2006). Electronic Statistics Textbook. Tulsa, OK: StatSoft.

6 Nearest Neighbours In this approach, the interpolated value Xi^ is defined as a weighted average of observed values of parameter X related to the nearest stations with applying a correction for differences in latitude, longitude and altitude: Xi^(λ,φ,h) = j=1,..,k wij [X(λj,φj,hj) + b (φ φj) + c (λ λj) + d (z zj)] where - wij is weight accounting for the distance dij between the two stations: wij = [1 (dij/d) 3 ] 3 ] for dij < D, wij = 0 for dij > D - b, c, d are parameters of the tri-variate linear regression model (X = a + b φ + c λ + d z), which are estimated using data from all available stations (except for the one, for which we interpolate during the cross-validation test) - D defines the surroundings of the location for which we interpolate. The value of D is a subject of optimisation. We found, that D~100 km is optimal for the present experiment. This technique requires an access to the database of the station-specific values of all WG parameters.

7 Data - Weather data: 125 stations from Czechia with data coverage greater or equal to 75% during (= at least 75% of terms have observed values of TMAX, TMIN, PREC, and CLOUD or SUNSHINE used to estimate SRAD) [location of the stations and their altitude is displayed in Figure 1] - Topography of Czechia is derived from the global digital elevation model GTOPO30 (horizontal grid spacing = 30 arc seconds; i.e. approximately m resolution within the territory of Czechia) [

8 Experiment A complete set of the Met&Roll s parameters for a single station includes a large number of characteristics, which account for the annual cycle. Of these, only 48 parameters of the precipitation sub-model (4 parameters 12 months) were interpolated using the three interpolation techniques. Of 125 available stations, 100 stations (= learning set) were used to calibrate the interpolation techniques, the remaining 25 stations were used for verification (= verification set). - Validation test: WG parameters for each station in the verification set are interpolated using WG data from the learning set and the station-specific altitude. Summary statistics and interpolation errors for selected WG parameters are given in Table I, station-specific errors are shown in Figures 2.x), - Data from the learning set of 100 stations were used to interpolate WG parameters into grid using the GTOPO30 topography. [Figures 2.x] - 30-year synthetic series were generated by site-calibrated WG (WG parameters are derived from the site-specific observes weather series) and interpolated WGs (neural networks and nearest neighbours techniques were used). Extreme precipitation characteristics were derived from the synthetic series and are compared with those derived from the observed weather series [Figures 3.x ] note: unfortunately, due to the error found in the settings of the experiment, we cannot present here extreme precipitation characteristics related to WG interpolated with GIS-based interpolation

9 Conclusions The poster presents results of experiments with interpolating weather generator parameters. Three interpolation techniques were tested: (a) neural networks, (b) nearest neighbours, (c) GIS-based cokriging. Following the validation scores given in Table I and station-specific errors shown in Figures 2.x, one could deduce that: - nearest neighbours provide results comparable to neural networks. But: (i) to use the nearest neighbours, all learning data must be available during interpolation into ungauged stations (in contrast with neural network, which do not need access to learning data after being trained). (ii) Both interpolation techniques may be subject of further improvements - GIS appears to provide results, which are generally slightly worse compared to the former two techniques. However, GIS was found to provide valuable help in finding other co-variates which could further improve the interpolation accuracy. Based on results shown in Figures 3.x, it is apparent that the errors in extreme precipitation characteristics related to interpolation of WG parameters (panels c vs. b, d vs. b) are lower than those related to imperfections of the precipitation model used in the generator. this poster will be soon available from the project web page:

10 References [1] Dubrovský, M., 1997: Creating Daily Weather Series with Use of the Weather Generator, Environmetrics 8, [2] Buchtele, J., Buchtelová, M., Fořtová, M., and Dubrovský, M., 1999: Runoff Changes in Czech River Basins - the Outputs of Rainfall-Runoff Simulations Using Different Climate Change Scenarios, J. Hydrol. Hydromech., 47, [3] Dubrovský M., Zalud Z. and Stastna M., 2000: Sensitivity of CERES-Maize yields to statistical structure of daily weather series. Climatic Change 46, [4] Huth R., Kyselý J., Dubrovský M., 2001: Time structure of observed, GCM-simulated, downscaled, and stochastically generated daily temperature series. Journal of Climate, 14, [5] Žalud Z., Dubrovský M., 2002: Modelling climate change impacts on maize growth and development in the Czech republic. Theoretical and Applied Climatology, 72, [6] Huth R., Kyselý J., Dubrovský M., 2003: Simulation of Surface Air Temperature by GCMs, Statistical Downscaling and Weather Generator: Higher-Order Statistical Moments. Studia Geophysica et Geodaetica 47, [7] Hejzlar J., Dubrovský M., Buchtele J. and Růžička M., 2003: The effect of climate change on the concentration of dissolved organic matter in a temperate stream (the Malše River, South Bohemia). Science of Total Environment [8] Dubrovský M., Buchtele J., Žalud Z., 2004: High-Frequency and Low-Frequency Variability in Stochastic Daily Weather Generator and Its Effect on Agricultural and Hydrologic Modelling. Climatic Change 63 (No.1-2), [9] Trnka M., Dubrovský M., Semerádova D., Žalud Z., 2004: Projections of uncertainties in climate change scenarios into expected winter wheat yields. Theoretical and Applied Climatology, 77, [10] Trnka M., Dubrovský M., Žalud Z., 2004: Climate Change Impacts and Adaptation Strategies in Spring Barley Production in the Czech Republic. Climatic Change 64 (No. 1-2), [11] Kyselý J., Dubrovský M., 2005: Simulation of extreme temperature events by a stochastic weather generator: effects of interdiurnal and interannual variability reproduction. Int.J.Climatol. 25,

11 Figure 1 Topography of Czechia and location of the 125 stations used for interpolation. Circles represent stations in the learning set, squares mark stations in the verification set. Altitude of the stations is represented by a colour according to the scale

12 Accuracy of the interpolation techniques

13 Table I. Summary of WG parameters characteristics and results of the validation of the three interpolation techniques (using a 25 stations verification set of stations different from the 100 learning stations). 125 stations Neural Networks avg std min max r2 (λ,φ,z) Nearest Neighbours simple co-kriging RMSE RV RMSE RV RMSE RV a) Precipitation: Γsh (shape parameter of the Gamma distribution) JAN APR JUL OCT b) Precipitation: Γsh Γsc (product of shape and scale parameters of the Gamma distribution) JAN APR JUL OCT c) Precipitation: probability of wet day occurrence JAN APR JUL OCT d) Precipitation: transitional prob. of wet day occurrence (given the previous day was dry) JAN APR JUL OCT e) Daily temperature maximum avg: JAN avg, JUL std*, JAN std*, JUL f) Daily temperature minimum avg: JAN avg, JUL std*, JAN std*, JUL legend: - avg, std, min, max: (average, standard deviation, minimum and maximum from the set of 125 stations) - r2(λ,φ,z) = multiple correlation coefficient of WG parameter with λ, φ, z - RMSE = Root mean square error - RV = reduction of variance - std* = standard deviation of the deviations from a mean annual cycle note: Γsh Γsc corresponds to the mean precipitation amount on a wet day

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15 Fig.2.1 Interpolation of Γ sh (Jul) a) Neural Networks b) Nearest Neighbours

16 c) Simple Cokriging

17 Fig.2.2 Interpolation of Γ sc Γ sh (Jul) a) Neural Networks b) Nearest Neighbours

18 c) Simple Cokriging

19 Fig.2.3 Interpolation of P wet (Jan.) a) Neural Networks b) Nearest Neighbours

20 c) Simple Cokriging

21 Fig.2.4 Interpolation of P wet dry (Jan) a) Neural Networks b) Nearest Neighbours

22 c) Simple Cokriging

23 Validation of weather generator (panels a vs. b) and interpolation techniques (panels c vs. b, d vs. b) using extreme precipitation characteristics

24 Fig.3.1 Annual max. length of dry spell a) observed weather b) site-calibrate weather generator

25 c) interpolated weather generator (Neural Networks) d) interpolated weather generator (Nearest Neighbours)

26 Fig year max. length of dry spell a) observed weather b) site-calibrate weather generator

27 c) interpolated weather generator (Neural Networks) d) interpolated weather generator (Nearest Neighbours)

28 Fig.3.3 Annual max. length of wet spell a) observed weather b) site-calibrate weather generator

29 c) interpolated weather generator (Neural Networks) d) interpolated weather generator (Nearest Neighbours)

30 Fig year max.length of wet spell a) observed weather b) site-calibrate weather generator

31 c) interpolated weather generator (Neural Networks) d) interpolated weather generator (Nearest Neighbours)

32 Fig.3.5 annual max. 1day precipitation a) observed weather b) site-calibrate weather generator

33 c) interpolated weather generator (Neural Networks) d) interpolated weather generator (Nearest Neighbours)

34 Fig year max. 1day precipitation a) observed weather b) site-calibrate weather generator

35 c) interpolated weather generator (Neural Networks) d) interpolated weather generator (Nearest Neighbours)

36 Fig.3.7 annual max 5-day precipitation a) observed weather b) site-calibrate weather generator

37 c) interpolated weather generator (Neural Networks) d) interpolated weather generator (Nearest Neighbours)

38 Fig year max 5-day precipitation a) observed weather b) site-calibrate weather generator

39 c) interpolated weather generator (Neural Networks) d) interpolated weather generator (Nearest Neighbours)