Sub-daily Statistical Downscaling of Meteorological Variables Using Neural Networks

AbstractA new open source neural network temporal downscaling model is described and tested using CRU-NCEP reanal ysis and CCSM3 climate model output. We downscaled multiple meteorological variables in tandem from monthly to sub-daily time steps while also retaining consistent correlations between variables. We found that our feed forward, error backpropagation approach produced synthetic 6 hourly meteorology with biases no greater than 0.6% across all variables and variance that was accurate within 1% for all variables except atmospheric pressure, wind speed, and precipitation. Correlations between downscaled output and the expected (original) monthly means exceeded 0.99 for all variables, which indicates that this approach would work well for generating atmospheric forcing data consistent with mass and energy conserved GCM output. Our neural network approach performed well for variables that had correlations to other variables of about 0.3 and better and its skill was increased by downscaling multiple correlated variables together. Poor replication of precipitation intensity however required further post-processing in order to obtain the expected probability distribution. The concurrence of precipitation events with expected changes in sub ordinate variables (e.g., less incident shortwave radiation during precipitation events) were nearly as consistent in the downscaled data as in the training data with probabilities that differed by no more than 6%. Our downscaling approach requires training data at the target time step and relies on a weak assumption that climate variability in the extrapolated data is similar to variability in the training data

Downscaling Temperature and Precipitation: A Comparison of Regression-Based Methods and Artificial Neural Networks

A comparison of two statistical downscaling methods for daily maximum and minimum surface air temperature, total daily precipitation and total monthly precipitation at Indianapolis, IN, USA, is presented. The analysis is conducted for two seasons, the growing season and the non-growing season, defined based on variability of surface air temperature. The predictors used in the downscaling are indices of the synoptic scale circulation derived from rotated principal components analysis (PCA) and cluster analysis of variables extracted from an 18-year record from seven rawinsonde stations in the Midwest region of the United States. PCA yielded seven significant components for the growing season and five significant components for the non-growing season. These PCs explained 86% and 83% of the original rawinsonde data for the growing and non-growing seasons, respectively. Cluster analysis of the PC scores using the average linkage method resulted in eight growing season synoptic types and twelve non-growing synoptic types. The downscaling of temperature and precipitation is conducted using PC scores and cluster frequencies in regression models and artificial neural networks (ANNs). Regression models and ANNs yielded similar results, but the data for each regression model violated at least one of the assumptions of regression analysis. As expected, the accuracy of the downscaling models for temperature was superior to that for precipitation. The accuracy of all temperature models was improved by adding an autoregressive term, which also changed the relative importance of the dominant anomaly patterns as manifest in the PC scores. Application of the transfer functions to model daily maximum and minimum temperature data from an independent time series resulted in correlation coefficients of 0.34–0.89. In accord with previous studies, the precipitation models exhibited lesser predictive capabilities. The correlation coefficient for predicted versus observed daily precipitation totals was less than 0.5 for both seasons, while that for monthly total precipitation was below 0.65. The downscaling techniques are discussed in terms of model performance, comparison of techniques and possible model improvements

Redes neuronales de convolución profundas para la regionalización estadística de proyecciones de cambio climático

RESUMEN Las proyecciones climáticas a escala local y/o regional son muy demandadas por diversos sectores socioeconómicos para elaborar sus planes de adaptación y mitigación al cambio climático. Sin embargo, los modelos climáticos globales actuales presentan una resolución espacial muy baja, lo que dificulta enormemente la elaboración de este tipo de estudios. Una manera de aumentar esta resolución es establecer relaciones estadísticas entre la variable local de interés (por ejemplo la temperatura y/o precipitación en una localidad dada) y un conjunto de variables de larga escala (por ejemplo, geopotencial y/o vientos en distintos niveles verticales) dadas por los modelos climáticos. En particular, en esta Tesis se explora la idoneidad de las redes neuronales de convolución (CNN) como método de downscaling estadístico para generar proyecciones de cambio climático a alta resolución sobre Europa. Para ello se evalúa primero la capacidad de estos modelos para reproducir la variabilidad local de precipitación y de temperatura en un período histórico reciente, comparándolas contra otros métodos estadísticos de referencia. A continuación, se analiza la idoneidad de estos modelos para regionalizar las proyecciones climáticas en el futuro (hasta el año 2100). Además, se desarrollan diversos estudios de interpretabilidad sobre redes neuronales para ganar confianza y conocimiento sobre el uso de este tipo de técnicas para aplicaciones climáticas, puesto que a menudo son rechazadas por ser consideradas “cajas negras”.ABSTRACT Regional climate projections are very demanded by different socioeconomics sectors to elaborate their adaptation and mitigation plans to climate change. Nevertheless, the state-of-the-art Global Glimate Models (GCMs) present very coarse spatial resolutions what limits their use in most of practical applications and impact studies. One way to increase this limited spatial resolution is to establish empirical/statistical functions which link the local variable of interest (e.g. temperature and/or precipitation at a given site) with a set of large-scale atmospheric variables (e.g. geopotential and/or winds at different vertical levels), which are typically well-reproduced by GCMs. In this context, this Thesis explores the suitability of deep learning, and in particular modern Convolutional Neural Networks (CNNs), as statistical downscaling techniques to produce regional climate change projections over Europe. To achieve this ambitious goal, the capacity of CNNs to reproduce the local variability of precipitation and temperature fields in present climate conditions is first assessed by comparing their performance with that from a set of traditional, benchmark statistical methods. Subsequently, their suitability to produce plausible future (up to 2100) high-resolution scenarios is put to the test by comparing their projected signals of change with those given by a set of state-of-the-art GCMs from CMIP5 and Regional Climate Models (RCMs) from the flagship EURO-CORDEX initiative. Also, a variety of interpretability techniques are also carried out to gain confidence and knowledge on the use of CNNs for climate applications, which have typically discarded until now for being considered as "black-boxes"

Climate across scales: the downscaling of precipitation for a basin in atropical mountain region in the Andes of Southern Ecuador

The main objective of this research is to study the downscaling of precipitation at basin scale in the Paute river basin, which is located in the tropical Andes of Southern Ecuador. The main assumption is that, by incorporating orographic information in the downscaling of precipitation, improved estimates of precipitation can be achieved. Such research is important, both from a scientific perspective as well as for water resource management and planning assessment for this developing country

Prediction of minimum temperatures in an alpine region by linear and non-linear post-processing of meteorological models

International audienceModel Output Statistics (MOS) refers to a method of post-processing the direct outputs of numerical weather prediction (NWP) models in order to reduce the biases introduced by a coarse horizontal resolution. This technique is especially useful in orographically complex regions, where large differences can be found between the NWP elevation model and the true orography. This study carries out a comparison of linear and non-linear MOS methods, aimed at the prediction of minimum temperatures in a fruit-growing region of the Italian Alps, based on the output of two different NWPs (ECMWF T511?L60 and LAMI-3). Temperature, of course, is a particularly important NWP output; among other roles it drives the local frost forecast, which is of great interest to agriculture. The mechanisms of cold air drainage, a distinctive aspect of mountain environments, are often unsatisfactorily captured by global circulation models. The simplest post-processing technique applied in this work was a correction for the mean bias, assessed at individual model grid points. We also implemented a multivariate linear regression on the output at the grid points surrounding the target area, and two non-linear models based on machine learning techniques: Neural Networks and Random Forest. We compare the performance of all these techniques on four different NWP data sets. Downscaling the temperatures clearly improved the temperature forecasts with respect to the raw NWP output, and also with respect to the basic mean bias correction. Multivariate methods generally yielded better results, but the advantage of using non-linear algorithms was small if not negligible. RF, the best performing method, was implemented on ECMWF prognostic output at 06:00 UTC over the 9 grid points surrounding the target area. Mean absolute errors in the prediction of 2 m temperature at 06:00 UTC were approximately 1.2°C, close to the natural variability inside the area itself

Development and Testing of Methods to Assess the Impact of Climate Change on Flood and Drought Risk at the European Scale

During the last 100 years global climate has warmed by an average of 0.6ºC, owing in part to human induced greenhouse gas emissions. Based on different scenarios of future greenhouse gas emissions projections of climate models indicate another 1.4 to 5.8 ºC of warming over the next century (IPCC, 2001a). The projected change in climate will significantly impact the hydrological cycle. A warmer climate will increase evaporation, the intensity of water cycling, and result in greater amounts of moisture in the air. It is expected that the magnitude and frequency of extreme weather events will increase, and that hydrological extremes such as floods and droughts will likely be more frequent and severe. The Joint Research Centre aims to develop knowledge and tools in support of the EU Climate Change Strategy that was recently put forward in the Commission’s Communication “Winning the Battle Against Global Climate Change” (COM(2005) 35). In view of this, an important research topic of the Land Management Unit of the IES is to assess the impact of climate change on the occurrence of hydrological extremes such as floods and droughts. This will be accomplished by developing an integrated modelling framework that combines regional climate predictions for Europe with the LISFLOOD model. LISFLOOD is a distributed, partially physically-based rainfall-runoff model that has been devised to simulate the hydrological behaviour in large European catchments (De Roo et al., 2000), with emphasis on predicting floods and droughts. Owing to its general nature, LISFLOOD is optimally suited for simulating the different hydrological regimes across Europe. Predicted climate for current conditions and for different scenarios of greenhouse gas emissions by the end of the 21st century will be used as input to LISFLOOD, after taking due account of any systematic bias in the climate forcing data obtained from climate models. Runoff statistics for the two periods will provide a means to estimate changes in the frequency and severity of hydrological extremes under different scenarios of future greenhouse gas emissions. Projections of future climate change are typically obtained from coupled Atmosphere-Ocean General Circulation Models (AOGCM). Because they require time steps of minutes but are used to predict climate change on time scales of months to centuries, their horizontal resolution is typically at least 100 km and hence their treatment of physical processes is approximate. Due to their coarse spatial resolution AOGCMs fail to explicitly capture fine-scale climatic structures needed for climate change impact studies and policy planning at the regional or sub-regional scale (e.g., catchment or basin scale). To resolve this problem, regionalization or downscaling methods have been developed that enhance regional detail and provide climatic information at smaller scales. The aim of this document is to provide an overview on existing methods for downscaling global climate information. Also, this document gives an overview of existing regional climate data sets for Europe, and details on how to use regional climate data for impact studies at the European and regional scale. The document is organised as follows. Section 2 presents a general overview of existing downscaling methods, with details of the underlying principles to generate regional climate information. In Section 3 an overview is given of regional climate data that are currently available to be used for impact studies at the European scale. In Section 4 some details are provided about the integrated modelling framework that couples the regional climate model data with the hydrological model LISFLOOD. Conclusions and an overview of current and further work are presented in Section 5.JRC.H.7-Land management and natural hazard