Weather modelling using a multivariate latent Gaussian model

We propose a vector autoregressive moving average process as a model for daily weather data. For the rainfall variable a monotonic transformation is applied to achieve marginal normality, thus defining a latent variable, with zero rainfall data corresponding to censored values below a threshold. Methodology is presented for model identification, estimation and validation, illustrated using data from Mynefield, Scotland. The new model, a VARMA(2,1) process, fits the data and produces more realistic simulated series than existing methods dur to Richardson (1981) and Peiris and McNicol (1996)

A joint probability approach to flood frequency estimation using Monte Carlo simulation

In the UK, flood estimation using event based rainfall–runoff modelling currently assigns pre-defined design values to the input variables which control the size of the flow events, apart from the rainfall magnitude which is treated as a random variable. The use of design values, rather than allowing the variables to be described by their full probability distribution, is a practical simplification but may lead to biases in the output flood magnitudes. The present study simulates a large number of flow events using sets of input variables from distributions fitted to observed event data, taking into account seasonality. These simulated datasets are used for running a rainfall-runoff model, and a frequency analysis is applied to the peaks of the output flow hydrographs. The simulated inputs are the rainfall intensity and duration, and the soil moisture deficit (SMD) and initial river flow at the beginning of the rainfall event. An inter-event arrival time is simulated so that a series of events is obtained. The initial conditions of SMD and river flow of each event are made dependent on the (simulated) time elapsed since the previous event, and on the SMD at the end of the previous event

Statistical Modeling of Spatial Extremes

The areal modeling of the extremes of a natural process such as rainfall or temperature is important in environmental statistics; for example, understanding extreme areal rainfall is crucial in flood protection. This article reviews recent progress in the statistical modeling of spatial extremes, starting with sketches of the necessary elements of extreme value statistics and geostatistics. The main types of statistical models thus far proposed, based on latent variables, on copulas and on spatial max-stable processes, are described and then are compared by application to a data set on rainfall in Switzerland. Whereas latent variable modeling allows a better fit to marginal distributions, it fits the joint distributions of extremes poorly, so appropriately-chosen copula or max-stable models seem essential for successful spatial modeling of extremes.Comment: Published in at http://dx.doi.org/10.1214/11-STS376 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Assessing spatio-temporal rainfall variability in a tropical mountain area (Ethiopia) using NOAA's rainfall estimates

Seasonal and interannual variation in rainfall can cause massive economic loss for farmers and pastoralists, not only because of deficient total rainfall amounts but also because of long dry spells within the rainy season. The semi-arid to sub-humid mountain climate of the North Ethiopian Highlands is especially vulnerable to rainfall anomalies. In this article, spatio-temporal rainfall patterns are analysed on a regional scale in the North Ethiopian Highlands using satellite-derived rainfall estimates (RFEs). To counter the weak correlation in the dry season, only the rainy season rainfall from March till September is used, responsible for approximately 91% of the annual rainfall. Validation analysis demonstrates that the RFEs are well correlated with the meteorological station (MS) rainfall data, i.e. 85% for RFE 1.0 (1996-2000) and 80% for RFE 2.0 (2001-2006). However, discrepancies indicate that RFEs generally underestimate MS rainfall and the scatter around the trendlines indicates that the estimation by RFEs can be in gross error. A local calibration of RFE with rain gauge information is validated as a technique to improve RFEs for a regional mountainous study area. Slope gradient, slope aspect, and elevation have no added value in the calibration of the RFEs. The estimation of monthly rainfall using this calibration model improved on average by 8%. Based upon the calibration model, annual rainfall maps and an average isohyet map for the period 1996-2006 were constructed. The maps show a general northeast-southwest gradient of increasing rainfall in the study area and a sharp east-west gradient in its northern part. Slope gradient, slope aspect, elevation, easting, and northing were evaluated as explanatory factors for the spatial variability of annual rainfall in a stepwise multiple regression with the calibrated average of RFE 1.0 as dependent variable. Easting and northing are the only significant contributing variables (R-2=0.86), of which easting has proved to be the most important factor (R-2=0.72). The scatter around the individual trendlines of easting and northing corresponds to an increase in rainfall variability in the drier regions. Despite the remaining underestimation of rainfall in the southern part of the study area, the improved estimation of spatio-temporal rainfall variability in a mountainous region by RFEs is valuable as input to a wide range of scientific models

Self-organizing nonlinear output (SONO): A neural network suitable for cloud patch-based rainfall estimation at small scales

Accurate measurement of rainfall distribution at various spatial and temporal scales is crucial for hydrological modeling and water resources management. In the literature of satellite rainfall estimation, many efforts have been made to calibrate a statistical relationship (including threshold, linear, or nonlinear) between cloud infrared (IR) brightness temperatures and surface rain rates (RR). In this study, an automated neural network for cloud patch-based rainfall estimation, entitled self-organizing nonlinear output (SONO) model, is developed to account for the high variability of cloud-rainfall processes at geostationary scales (i.e., 4 km and every 30 min). Instead of calibrating only one IR-RR function for all clouds the SONO classifies varied cloud patches into different clusters and then searches a nonlinear IR-RR mapping function for each cluster. This designed feature enables SONO to generate various rain rates at a given brightness temperature and variable rain/no-rain IR thresholds for different cloud types, which overcomes the one-to-one mapping limitation of a single statistical IR-RR function for the full spectrum of cloud-rainfall conditions. In addition, the computational and modeling strengths of neural network enable SONO to cope with the nonlinearity of cloud-rainfall relationships by fusing multisource data sets. Evaluated at various temporal and spatial scales, SONO shows improvements of estimation accuracy, both in rain intensity and in detection of rain/no-rain pixels. Further examination of the SONO adaptability demonstrates its potentiality as an operational satellite rainfall estimation system that uses the passive microwave rainfall observations from low-orbiting satellites to adjust the IR-based rainfall estimates at the resolution of geostationary satellites. Copyright 2005 by the American Geophysical Union

Changing rainfall pattern in Northeast Thailand and implications for cropping systems adaptation

In Northeast Thailand, about 80% of the 20 million inhabitants are engaged in rainfed agriculture. Climate vagaries combined with coarse-textured sandy and unevenly distributed saline soils explain low agriculture yields and the endemic relative poverty of the population. We conducted an in-depth analysis of change in the rainfall pattern using daily records (1953-2010) from 18 gauging stations scattered across Northeast Thailand. Based on an intimate knowledge of the local farming systems, particularly their strategies to deal with climate variability and their evolution during the past decades, we analyse and discuss how the cropping systems can adapt to the detected rainfall changes. We used the Mann–Kendall trend detection test, modified to account for serial correlation at each individual station, and the regional average Kendall's statistic designed for the detection of regional trends across the entire studied area. On-farm surveys carried out during the past two decades in both the upper and lower parts of Northeast Thailand provide a detailed understanding of the functioning of the agricultural production systems and their diversity. The analysis reveals very limited changes in rainfall frequency, intensity and extremes during the humid monsoon and therefore little change in the existing climatic constraints to agricultural production (early dry spells in the wet season and risk of floods at its peak in September). But we found a significant regional trend toward a wetter dry season that could offer new limited opportunities for agricultural production. The paper will discuss the implications of these findings and compare them with recently published research results. Differences in statistical significance between local and regional rainfall trends are also interpreted. If these trends extend, households would not face many difficulties because of their renowned adaptive capacity built over centuries of facing highly variable rainfall patterns, and due to the diversity of their resilient farming systems. (Texte intégral

Stochastic urban pluvial flood hazard maps based upon a spatial-temporal rainfall generator

It is a common practice to assign the return period of a given storm event to the urban pluvial flood event that such storm generates. However, this approach may be inappropriate as rainfall events with the same return period can produce different urban pluvial flooding events, i.e., with different associated flood extent, water levels and return periods. This depends on the characteristics of the rainfall events, such as spatial variability, and on other characteristics of the sewer system and the catchment. To address this, the paper presents an innovative contribution to produce stochastic urban pluvial flood hazard maps. A stochastic rainfall generator for urban-scale applications was employed to generate an ensemble of spatially—and temporally—variable design storms with similar return period. These were used as input to the urban drainage model of a pilot urban catchment (~9 km2) located in London, UK. Stochastic flood hazard maps were generated through a frequency analysis of the flooding generated by the various storm events. The stochastic flood hazard maps obtained show that rainfall spatial-temporal variability is an important factor in the estimation of flood likelihood in urban areas. Moreover, as compared to the flood hazard maps obtained by using a single spatially-uniform storm event, the stochastic maps generated in this study provide a more comprehensive assessment of flood hazard which enables better informed flood risk management decisions

2013 Oregon Wine Harvest Report

This harvest report for the Oregon wine industry from 2013 describes the season as a play in two acts, with a wet intermission. Vintners were surprised by record rainfall one weekend in late September after a warm and dry summer. The rains, remnants from a typhoon in Japan, mostly affected the Willamette Valley, but effects were variable even within that region; southern and eastern Oregon were less affected by the storm

Heavy Rainfall Warning Assessment Tool User Guide. Version 1.2

This report is a User Guide to a PC tool for assessing Heavy Rainfall Warnings. Development of the PC tool formed an important operational output of the Environment Agency and Met Office funded project: "Development of Rainfall Forecast Performance Monitoring Criteria. Phase 1: Development of Methodology and Algorithms" (Jones et al., 2003). The Heavy Rainfall Warning (HRW) Assessment Tool is a toolkit for Microsoft Excel. The tool allows the user to configure an assessment framework for a particular format of Heavy Rainfall Warning, enter and save data for forecasts and ground-truths, and generate a range of performance measures and other statistics for new and previously saved data. Summary tables are presented using Excel's PivotTable feature, from which charts can also be generated. Performance measures are provided to assess forecasts of heavy rainfall in continuous variable, categorical and probability form: these include bias, rmse, R-squared Efficiency, skill scores and the Continuous Brier Score

VARIABLE RATE NITROGEN APPLICATION ON CORN FIELDS: THE ROLE OF SPATIAL VARIABILITY AND WEATHER

Meta-response functions for corn yields and nitrogen losses were estimated from EPIC-generated data for three soil types and three weather scenarios. These metamodels were used to evaluate variable rate (VRT) versus uniform rate (URT) nitrogen application technologies for alternative weather scenarios and policy option. Except under very dry conditions, returns per acre for VRT were higher than for URT and the economic advantage of VRT increased as realized rainfall decreased from expected average rainfall. Nitrogen losses to the environment from VRT were lower for all situation examined, except on fields with little spatial variability.Corn, environment, meta-response functions, nitrogen restriction, precision farming, site-specific management, spatial variability, weather variability, Crop Production/Industries,