Amir AghaKouchak

Global warming and changes in risk of concurrent climate extremes: Insights from the 2014 California drought

Global warming and the associated rise in extreme temperatures substantially increase the chance of concurrent droughts and heat waves. The 2014 California drought is an archetype of an event characterized by not only low precipitation but also extreme high temperatures. From the raging wildfires, to record low storage levels and snowpack conditions, the impacts of this event can be felt throughout California. Wintertime water shortages worry decision-makers the most because it is the season to build up water supplies for the rest of the year. Here we show that the traditional univariate risk assessment methods based on precipitation condition may substantially underestimate the risk of extreme events such as the 2014 California drought because of ignoring the effects of temperature. We argue that a multivariate viewpoint is necessary for assessing risk of extreme events, especially in a warming climate. This study discusses a methodology for assessing the risk of concurrent extremes such as droughts and extreme temperatures.

Evaluation of satellite‐retrieved extreme precipitation rates across the central United States

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 116, D02115, doi:10.1029/2010JD014741, 2011 Evaluation of satellite‐retrieved extreme precipitation rates across the central United States A. AghaKouchak, 1 A. Behrangi, 2 S. Sorooshian, 1 K. Hsu, 1 and E. Amitai 3,4 Received 10 July 2010; revised 10 September 2010; accepted 27 October 2010; published 26 January 2011. [ 1 ] Water resources management, forecasting, and decision making require reliable estimates of precipitation. Extreme precipitation events are of particular importance because of their severe impact on the economy, the environment, and the society. In recent years, the emergence of various satellite‐retrieved precipitation products with high spatial resolutions and global coverage have resulted in new sources of uninterrupted precipitation estimates. However, satellite‐based estimates are not well integrated into operational and decision‐making applications because of a lack of information regarding the associated uncertainties and reliability of these products. In this study, four satellite‐ derived precipitation products (CMORPH, PERSIANN, TMPA‐RT, and TMPA‐V6) are evaluated with respect to their performance in capturing precipitation extremes. The Stage IV (radar‐based, gauge‐adjusted) precipitation estimates are used as reference data. The results show that with respect to the probability of detecting extremes and the volume of correctly identified precipitation, CMORPH and PERSIANN data sets lead to better estimates. However, their false alarm ratio and volume are higher than those of TMPA‐RT and TMPA‐V6. Overall, no single precipitation product can be considered ideal for detecting extreme events. In fact, all precipitation products tend to miss a significant volume of rainfall. With respect to verification metrics used in this study, the performance of all satellite products tended to worsen as the choice of extreme precipitation threshold increased. The analyses suggest that extensive efforts are necessary to develop algorithms that can capture extremes more reliably. Citation: AghaKouchak, A., A. Behrangi, S. Sorooshian, K. Hsu, and E. Amitai (2011), Evaluation of satellite‐retrieved extreme precipitation rates across the central United States, J. Geophys. Res., 116, D02115, doi:10.1029/2010JD014741. 1. Introduction [ 2 ] Precipitation plays a significant role in weather research, monitoring, and predictions. Improving our understanding of weather and climate, along with the development of reliable and uninterrupted measurements, are essential for proper assessment of weather conditions. Currently, in situ and radar‐based precipitation data are the major input for streamflow forecasts, flash flood warnings, and weather watches across the United States. While some regions have long‐term historical in situ precipitation measurements, poor spatial sampling makes the data inadequate to support monitoring, detection, and forecast studies. On the other hand, in most parts of the globe (except in a few developed countries), radar installations for precipitation measurements Department of Civil and Environmental Engineering, University of California, Irvine, California, USA. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA. NASA Goddard Space Flight Center, Greenbelt, Maryland, USA. School of Earth and Environmental Sciences, Chapman University, Orange, California, USA. Copyright 2011 by the American Geophysical Union. 0148‐0227/11/2010JD014741 are not available. In the United States, with one of the most sophisticated radar measurement networks in the world, regions with extensive topographic relief (e.g., the western and southwestern United States) suffer from poor or non- existent radar coverage [Maddox et al., 2002]. In fact, Maddox et al. [2002] showed that at lower levels (e.g., 1000 m above ground level), which are closer estimates to ground‐level precipitation, the radar coverage area is sub- stantially smaller than at higher levels (e.g., 3000 m above ground level). [ 3 ] Clearly, the lack or absence of ground‐based precipi- tation networks hampers the development and use of flood and drought warning models, hydrological models, and extreme weather monitoring and decision‐making systems. Therefore, there exists the need to achieve alternative esti- mates of precipitation with sufficient sampling density, reliability, and accuracy to enable utilization of data for operational applications. Satellite‐derived precipitation esti- mates have the potential to improve precipitation observation at a global scale. In recent years, the National Aeronautics and Space Administration (NASA), National Oceanic and Atmospheric Administration (NOAA), and many other inter- national sponsored satellite missions have led to an increase in available precipitation data. These remotely sensed data have several advantages over in situ measurements, including D02115 1 of 11

Global integrated drought monitoring and prediction system

Drought is by far the most costly natural disaster that can lead to widespread impacts, including water and food crises. Here we present data sets available from the Global Integrated Drought Monitoring and Prediction System (GIDMaPS), which provides drought information based on multiple drought indicators. The system provides meteorological and agricultural drought information based on multiple satellite-, and model-based precipitation and soil moisture data sets. GIDMaPS includes a near real-time monitoring component and a seasonal probabilistic prediction module. The data sets include historical drought severity data from the monitoring component, and probabilistic seasonal forecasts from the prediction module. The probabilistic forecasts provide essential information for early warning, taking preventive measures, and planning mitigation strategies. GIDMaPS data sets are a significant extension to current capabilities and data sets for global drought assessment and early warning. The presented data sets would be instrumental in reducing drought impacts especially in developing countries. Our results indicate that GIDMaPS data sets reliably captured several major droughts from across the globe.

Advanced Concepts on Remote Sensing of Precipitation at Multiple Scales

ADVANCED CONCEPTS ON REMOTE SENSING OF PRECIPITATION AT MULTIPLE SCALES by S oroosh S orooshian , A mir A gha K ouchak , P hillip A rkin , J ohn E ylander , E fi F oufoula -G eorgiou , R ussell H armon , J an M. H. H endrickx , B isher I mam , R obert K uligowski , B rian S kahill , and G ail S kofronick -J ackson Overview of Recommendations (i) Uncertainty of merged products and multisensor observations warrants a great deal of research. Quantification of uncertainties and their propa- gation into combined products is vital for future development. (ii) Future improvements in satellite-based precipi- tation retrieval algorithms will rely on more in- depth research on error properties in different climate regions, storm regimes, surface condi- tions, seasons, and altitudes. Given such infor- mation, precipitation algorithms for retrieval, AFFILIATIONS : S orooshian , A gha K ouchak , I mam —University of California, Irvine, Irvine, California; A rkin —University of Maryland, College Park, Maryland; E ylander —U.S. Army Engineer Research and Development Center, Hanover, New Hampshire; F oufoula -G eorgiou —University of Minnesota, Minneapolis, Minnesota; H armon —Army Research Laboratory, Durham, North Carolina; H endrickx —New Mexico Tech, Socorro, New Mexico; K uligowski —NOAA/NESDIS/ STAR, Camp Springs, Maryland; S kahill —U.S. Army Engineer Research and Development Center, Vicksburg, Mississippi; S kofronick -J ackson —NASA GSFC, Greenbelt, Maryland CORRESPONDING AUTHOR : Soroosh Sorooshian, Department of Civil & Environmental Engineering, University of California, Irvine, Irvine, CA 92697 E-mail: soroosh@uci.edu DOI:10.1175/2011BAMS3158.1 In final form 18 April 2011

Remote sensing of drought: Progress, challenges and opportunities

This review surveys current and emerging drought monitoring approaches using satellite remote sensing observations from climatological and ecosystem perspectives. We argue that satellite observations not currently used for operational drought monitoring, such as near-surface air relative humidity data from the Atmospheric Infrared Sounder mission, provide opportunities to improve early drought warning. Current and future satellite missions offer opportunities to develop composite and multi-indicator drought models. While there are immense opportunities, there are major challenges including data continuity, unquantified uncertainty, sensor changes, and community acceptability. One of the major limitations of many of the currently available satellite observations is their short length of record. A number of relevant satellite missions and sensors (e.g., the Gravity Recovery and Climate Experiment) provide only a decade of data, which may not be sufficient to study droughts from a climate perspective. However, they still provide valuable information about relevant hydrologic and ecological processes linked to this natural hazard. Therefore, there is a need for models and algorithms that combine multiple data sets and/or assimilate satellite observations into model simulations to generate long-term climate data records. Finally, the study identifies a major gap in indicators for describing drought impacts on the carbon and nitrogen cycle, which are fundamental to assessing drought impacts on ecosystems.

Changes in concurrent monthly precipitation and temperature extremes

While numerous studies have addressed changes in climate extremes, analyses of concurrence of climate extremes are scarce, and climate change effects on joint extremes are rarely considered. This study assesses the occurrence of joint (concurrent) monthly continental precipitation and temperature extremes in Climate Research Unit (CRU) and University of Delaware (UD) observations, and in 13 Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate simulations. The joint occurrences of precipitation and temperature extremes simulated by CMIP5 climate models are compared with those derived from the CRU and UD observations for warm/wet, warm/dry, cold/wet, and cold/dry combinations of joint extremes. The number of occurrences of these four combinations during the second half of the 20th century (1951–2004) is assessed on a common global grid. CRU and UD observations show substantial increases in the occurrence of joint warm/dry and warm/wet combinations for the period 1978–2004 relative to 1951–1977. The results show that with respect to the sign of change in the concurrent extremes, the CMIP5 climate model simulations are in reasonable overall agreement with observations. However, the results reveal notable discrepancies between regional patterns and the magnitude of change in individual climate model simulations relative to the observations of precipitation and temperature.

Non-stationary extreme value analysis in a changing climate

This paper introduces a framework for estimating stationary and non-stationary return levels, return periods, and risks of climatic extremes using Bayesian inference. This framework is implemented in the Non-stationary Extreme Value Analysis (NEVA) software package, explicitly designed to facilitate analysis of extremes in the geosciences. In a Bayesian approach, NEVA estimates the extreme value parameters with a Differential Evolution Markov Chain (DE-MC) approach for global optimization over the parameter space. NEVA includes posterior probability intervals (uncertainty bounds) of estimated return levels through Bayesian inference, with its inherent advantages in uncertainty quantification. The software presents the results of non-stationary extreme value analysis using various exceedance probability methods. We evaluate both stationary and non-stationary components of the package for a case study consisting of annual temperature maxima for a gridded global temperature dataset. The results show that NEVA can reliably describe extremes and their return levels.

A Nonparametric Multivariate Multi-Index Drought Monitoring Framework

AbstractAccurate and reliable drought monitoring is essential to drought mitigation efforts and reduction of social vulnerability. A variety of indices, such as the standardized precipitation index (SPI), are used for drought monitoring based on different indicator variables. Because of the complexity of drought phenomena in their causation and impact, drought monitoring based on a single variable may be insufficient for detecting drought conditions in a prompt and reliable manner. This study outlines a multivariate, multi-index drought monitoring framework, namely, the multivariate standardized drought index (MSDI), for describing droughts based on the states of precipitation and soil moisture. In this study, the MSDI is evaluated against U.S. Drought Monitor (USDM) data as well as the commonly used standardized indices for drought monitoring, including detecting drought onset, persistence, and spatial extent across the continental United States. The results indicate that MSDI includes attractive propert...

Temperature impacts on the water year 2014 drought in California

©2015. American Geophysical Union. California is experiencing one of the worst droughts on record. We use a hydrological model and risk assessment framework to understand the influence of temperature on the water year (WY) 2014 drought in California and examine the probability that this drought would have been less severe if temperatures resembled the historical climatology. Our results indicate that temperature played an important role in exacerbating the WY 2014 drought severity. We found that if WY 2014 temperatures resembled the 1916-2012 climatology, there would have been at least an 86% chance that winter snow water equivalent and spring-summer soil moisture and runoff deficits would have been less severe than the observed conditions. We also report that the temperature forecast skill in California for the important seasons of winter and spring is negligible, beyond a lead time of 1month, which we postulate might hinder skillful drought prediction in California.

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