Ali Mehran

Systematic and random error components in satellite precipitation data sets

GEOPHYSICAL RESEARCH LETTERS, VOL. 39, L09406, doi:10.1029/2012GL051592, 2012 Systematic and random error components in satellite precipitation data sets Amir AghaKouchak, 1 Ali Mehran, 1 Hamidreza Norouzi, 2 and Ali Behrangi 3 Received 7 March 2012; revised 13 April 2012; accepted 13 April 2012; published 11 May 2012. [ 1 ] This study contributes to characterization of satellite precipitation error which is fundamental to develop uncertainty models and bias reduction algorithms. Systematic and random error components of several satellite precipitation products are investigated over different seasons, thresholds and temporal accumulations. The analyses show that the spatial distribution of systematic error has similar patterns for all precipitation products. However, the systematic (random) error of daily accumulations is significantly less (more) than that of high resolution 3-hr data. One should note that the systematic biases of satellite precipitation are distinctively different in the summer and winter. The systematic (random) error is remarkably higher (lower) during the winter. Furthermore, the systematic error seems to be proportional to the rain rate magnitude. The findings of this study highlight that bias removal methods should take into account the spatiotemporal characteristics of error as well as the proportionality of error to the magnitude of rain rate. Citation: AghaKouchak, A., A. Mehran, H. Norouzi, and A. Behrangi (2012), Systematic and random error components in satellite precipitation data sets, Geophys. Res. Lett., 39, L09406, doi:10.1029/2012GL051592. 1. Introduction [ 2 ] Over the past three decades, development of satellite sensors have resulted in multiple sources of precipitation data sets. However, the quantification and understanding of uncertainties associated with remotely sensed satellite data remains a challenging research topic Bellerby and Sun [2005]. The uncertainties of satellite precipitation data arise from different factors including the sensor itself, retrieval error, and spatial and temporal sampling, among others [e.g., Hong et al., 2006]. [ 3 ] Numerous studies have addressed validation, verifi- cation and uncertainty of satellite precipitation estimates against ground-based measurements [e.g., Turk et al., 2008; Ebert et al., 2007]. This study aims to go beyond the vali- dation and inter-comparison of satellite products by analyz- ing error characteristics of precipitation algorithms. In this Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California, USA. Department of Construction Management and Civil Engineering Technology, City University of New York, New York City College of Technology, Brooklyn, New York, USA. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA. paper, systematic and random error components of several satellite precipitation products are investigated over different seasons, thresholds and temporal accumulations. Ideally, the systematic error is to be removed or minimized. In mea- surement theory, many algorithms have been developed to reduce systematic error with the aim of reducing the overall uncertainty Taylor [1999]. Evidently, understanding error properties including systematic and random components are fundamental for future improvements in precipitation retrieval algorithms, development of uncertainty models and bias adjustment techniques, and many other research studies and operational applications [Sorooshian et al., 2011]. 2. Data Resources [ 4 ] The following satellite precipitation data sets are used for error analysis: (a) The CPC MORPHing (CMORPH) [Joyce et al., 2004] algorithm; (b) The Precipitation Esti- mation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) [Sorooshian et al., 2000]; (c) The Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) real-time (hereafter, 3b42-RT) [Huffman et al., 2007]. [ 5 ] The Stage IV radar-based gauge-adjusted precipita- tion data, available from the National Center for Environ- mental Prediction (NCEP), are used as the reference data set. The Stage IV data include merged operational radar data and rain gauge measurements in hourly accumulations and 4 km grids. The Stage IV observations are accumulated to 3-hourly and aggregated onto 0.25 grids to match with satellite data. The study area covers the entire conterminous United States (hereafter, CONUS). Three years of precipi- tation data (01/01/2005–12/31/2007) are used for the anal- ysis. Hereafter, the difference between satellite estimates and Stage IV observations is termed as precipitation error. 3. Methodology and Results [ 6 ] In this study, the Willmott decomposition technique is used for deriving the systematic and random components of error. Willmott [1981] suggested that the error in the numerical weather prediction models can be separated into systematic and random error components as: n A X n A X n ¼ i¼1 i¼1 Corresponding author: A. AghaKouchak, Department of Civil and Environmental Engineering, University of California, Irvine, CA 92617, USA. (amir9a@gmail.com) Copyright 2012 by the American Geophysical Union. 0094-8276/12/2012GL051592 P sat A P ref P* sat A P ref n A X i¼1 P sat A P* sat n n where: P sat = satellite estimates P ref = reference measurements (here, Stage IV) L09406 1 of 4

Increasing probability of mortality during Indian heat waves

An increase of 0.5°C in summer mean temperatures increases the probability of mass heat-related mortality in India by 146%. Rising global temperatures are causing increases in the frequency and severity of extreme climatic events, such as floods, droughts, and heat waves. We analyze changes in summer temperatures, the frequency, severity, and duration of heat waves, and heat-related mortality in India between 1960 and 2009 using data from the India Meteorological Department. Mean temperatures across India have risen by more than 0.5°C over this period, with statistically significant increases in heat waves. Using a novel probabilistic model, we further show that the increase in summer mean temperatures in India over this period corresponds to a 146% increase in the probability of heat-related mortality events of more than 100 people. In turn, our results suggest that future climate warming will lead to substantial increases in heat-related mortality, particularly in developing low-latitude countries, such as India, where heat waves will become more frequent and populations are especially vulnerable to these extreme temperatures. Our findings indicate that even moderate increases in mean temperatures may cause great increases in heat-related mortality and support the efforts of governments and international organizations to build up the resilience of these vulnerable regions to more severe heat waves.

A hybrid framework for assessing socioeconomic drought: Linking climate variability, local resilience, and demand

Socioeconomic drought broadly refers to conditions whereby the water supply cannot satisfy the demand. Most previous studies describe droughts based on large scale meteorological/hydrologic conditions, ignoring the demand and local resilience to cope with climate variability. Reservoirs provide resilience against climatic extremes and play a key role in water supply and demand management. Here, we outline a unique multivariate approach as a measure of socioeconomic drought, termed Multivariate Standardized Reliability and Resilience Index (MSRRI). The model combines information on the inflow and reservoir storage relative to the demand. MSRRI combines: (I) a “top-down” approach that focuses on processes/phenomena that cannot be simply controlled or altered by decision makers, such as climate change and variability, and (II) a “bottom-up” methodology that represents the local resilience and societal capacity to respond or adapt to droughts. MSRRI is based on a nonparametric multivariate distribution function that links Inflow-Demand Reliability (IDR) indicator to Water Storage Resilience (WSR) indicator. These indicators are used to assess socioeconomic drought during the Australian Millennium Drought (1998–2010) and the 2011–2014 California Drought. The results show that MSRRI is superior to univariate indices because it captures both early onset and persistence of water stress over time. The suggested framework can be applied to both individual reservoirs and a group of reservoirs in a region, and it is consistent with the currently available standardized drought indicators. MSRRI provides complementary information on socioeconomic drought development and recovery based on reservoir storage and demand that cannot be achieved from the commonly used drought indicators.

Compounding Impacts of Human-Induced Water Stress and Climate Change on Water Availability

The terrestrial phase of the water cycle can be seriously impacted by water management and human water use behavior (e.g., reservoir operation, and irrigation withdrawals). Here we outline a method for assessing water availability in a changing climate, while explicitly considering anthropogenic water demand scenarios and water supply infrastructure designed to cope with climatic extremes. The framework brings a top-down and bottom-up approach to provide localized water assessment based on local water supply infrastructure and projected water demands. When our framework is applied to southeastern Australia we find that, for some combinations of climatic change and water demand, the region could experience water stress similar or worse than the epic Millennium Drought. We show considering only the influence of future climate on water supply, and neglecting future changes in water demand and water storage augmentation might lead to opposing perspectives on future water availability. While human water use can significantly exacerbate climate change impacts on water availability, if managed well, it allows societies to react and adapt to a changing climate. The methodology we present offers a unique avenue for linking climatic and hydrologic processes to water resource supply and demand management and other human interactions.