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1. Introduction Accurate forecasting of hydrologic extreme events plays a significant role in developing appropriate policies to plan for available water resources. Although several studies have proposed promising methods to improve hydrologic forecasts, the observed effects of climate change on floods and droughts across different regions of the globe in recent decades highlights the need for more sophisticated methods in predicting extreme events ( Mishra and Singh 2010 ; Moradkhani et al
1. Introduction Accurate forecasting of hydrologic extreme events plays a significant role in developing appropriate policies to plan for available water resources. Although several studies have proposed promising methods to improve hydrologic forecasts, the observed effects of climate change on floods and droughts across different regions of the globe in recent decades highlights the need for more sophisticated methods in predicting extreme events ( Mishra and Singh 2010 ; Moradkhani et al
parts of Ethiopia, Djibouti, Somalia, and northern Kenya. The related population displacement and price increase of food and fuel resulted in a serious humanitarian crisis ( Dutra et al. 2012 ; Peterson et al. 2012 ). Therefore, the provision of seasonal forecasts that can provide sufficient early warning has the potential to help the local governments and nongovernmental organizations (NGOs) to move from management of drought crises to management of drought risk, increasing the resilience to
parts of Ethiopia, Djibouti, Somalia, and northern Kenya. The related population displacement and price increase of food and fuel resulted in a serious humanitarian crisis ( Dutra et al. 2012 ; Peterson et al. 2012 ). Therefore, the provision of seasonal forecasts that can provide sufficient early warning has the potential to help the local governments and nongovernmental organizations (NGOs) to move from management of drought crises to management of drought risk, increasing the resilience to
, recreation economies, energy, and ecosystems. Recognizing the economic and social impacts from drought, the U.S. Congress in 2006 passed the National Integrated Drought Information System Act of 2006 (Public Law 109-430) with NOAA as the lead agency. The subsequent NIDIS Implementation Plan was developed to “[f]oster, and support, a research environment that focuses on risk assessment, forecasting, and management,” among other goals ( NIDIS 2007 , p. iii). Given its widespread support, NIDIS was
, recreation economies, energy, and ecosystems. Recognizing the economic and social impacts from drought, the U.S. Congress in 2006 passed the National Integrated Drought Information System Act of 2006 (Public Law 109-430) with NOAA as the lead agency. The subsequent NIDIS Implementation Plan was developed to “[f]oster, and support, a research environment that focuses on risk assessment, forecasting, and management,” among other goals ( NIDIS 2007 , p. iii). Given its widespread support, NIDIS was
updrafts . Mon. Wea. Rev. , 131 , 2394 – 2416 , https://doi.org/10.1175/1520-0493(2003)131<2394:RRFTSO>2.0.CO;2 . 10.1175/1520-0493(2003)131<2394:RRFTSO>2.0.CO;2 Bunkers , M. J. , B. A. Klimowski , R. L. Thompson , and M. L. Weisman , 2000 : Predicting supercell motion using a new hodograph technique . Wea. Forecasting , 15 , 61 – 79 , https://doi.org/10.1175/1520-0434(2000)015<0061:PSMUAN>2.0.CO;2 . 10.1175/1520-0434(2000)015<0061:PSMUAN>2.0.CO;2 Dahl , J. M. L. , 2017 : Tilting
updrafts . Mon. Wea. Rev. , 131 , 2394 – 2416 , https://doi.org/10.1175/1520-0493(2003)131<2394:RRFTSO>2.0.CO;2 . 10.1175/1520-0493(2003)131<2394:RRFTSO>2.0.CO;2 Bunkers , M. J. , B. A. Klimowski , R. L. Thompson , and M. L. Weisman , 2000 : Predicting supercell motion using a new hodograph technique . Wea. Forecasting , 15 , 61 – 79 , https://doi.org/10.1175/1520-0434(2000)015<0061:PSMUAN>2.0.CO;2 . 10.1175/1520-0434(2000)015<0061:PSMUAN>2.0.CO;2 Dahl , J. M. L. , 2017 : Tilting
1. Introduction In the fall of 2010 the U.S. Drought Monitor showed no areas of the United States in drought, a situation essentially unique since the Drought Monitor was initiated in 1999. However, even as the Drought Monitor was showing unusually moist conditions across the country, seasonal-to-interannual forecasts were predicting a return to dry conditions across the southern United States and northern Mexico in the winter ahead. Those forecasts were based on predictions of a developing La
1. Introduction In the fall of 2010 the U.S. Drought Monitor showed no areas of the United States in drought, a situation essentially unique since the Drought Monitor was initiated in 1999. However, even as the Drought Monitor was showing unusually moist conditions across the country, seasonal-to-interannual forecasts were predicting a return to dry conditions across the southern United States and northern Mexico in the winter ahead. Those forecasts were based on predictions of a developing La
temperatures for the entire 1895–2011 period (Fig. ES4) reveals no statistically significant relationship. The lack of such relationships between summer U.S. precipitation and sea surface temperatures has thwarted efforts at successful seasonal forecasting. Global SSTs have appreciably changed, however, since the occurrence of past major central plains droughts. Figure 6 presents two analyses for the SST anomalies of May–August 2012: one calculated relative to a 1901–90 climatology (top) that brackets
temperatures for the entire 1895–2011 period (Fig. ES4) reveals no statistically significant relationship. The lack of such relationships between summer U.S. precipitation and sea surface temperatures has thwarted efforts at successful seasonal forecasting. Global SSTs have appreciably changed, however, since the occurrence of past major central plains droughts. Figure 6 presents two analyses for the SST anomalies of May–August 2012: one calculated relative to a 1901–90 climatology (top) that brackets
damage, it is important to understand the current prediction capability within the region. It is now fairly well understood that a multimodel approach to prediction is an imperfect but still pragmatic method to estimating forecast uncertainty ( Krishnamurti et al. 1999 , 2000 ; Doblas-Reyes et al. 2000 ; Palmer et al. 2004 ; Hagedorn et al. 2005 ; Weigel et al. 2008 ; Kirtman and Min 2009 ). In this paper we utilize phase-1 data from the North American Multi-Model Ensemble (NMME) system, a
damage, it is important to understand the current prediction capability within the region. It is now fairly well understood that a multimodel approach to prediction is an imperfect but still pragmatic method to estimating forecast uncertainty ( Krishnamurti et al. 1999 , 2000 ; Doblas-Reyes et al. 2000 ; Palmer et al. 2004 ; Hagedorn et al. 2005 ; Weigel et al. 2008 ; Kirtman and Min 2009 ). In this paper we utilize phase-1 data from the North American Multi-Model Ensemble (NMME) system, a
. From 2007 to 2013, Lloyd-Hughes and Saunders (2007) operated a global drought monitor, which was updated on a monthly basis and used station-based precipitation from the Global Precipitation Climatology Centre (GPCC; Schneider et al. 2014 ) and air temperature from the European Centre for Medium-Range Weather Forecasts (ECMWF). Their system used the standardized precipitation index (SPI) and the Palmer drought severity index (PDSI). Princeton University operates an African drought monitor
. From 2007 to 2013, Lloyd-Hughes and Saunders (2007) operated a global drought monitor, which was updated on a monthly basis and used station-based precipitation from the Global Precipitation Climatology Centre (GPCC; Schneider et al. 2014 ) and air temperature from the European Centre for Medium-Range Weather Forecasts (ECMWF). Their system used the standardized precipitation index (SPI) and the Palmer drought severity index (PDSI). Princeton University operates an African drought monitor
. (2011) , MERRA is an improvement over the previous generation of reanalyses and is in many aspects comparable to the other new reanalyses such as the Interim European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-Interim) ( Dee et al. 2011 ) and the National Oceanic and Atmospheric Administration Climate Forecast System Reanalysis (CFSR) ( Saha et al. 2010 ). There are, however, still substantial uncertainties and differences between these new reanalyses in poorly constrained quantities
. (2011) , MERRA is an improvement over the previous generation of reanalyses and is in many aspects comparable to the other new reanalyses such as the Interim European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-Interim) ( Dee et al. 2011 ) and the National Oceanic and Atmospheric Administration Climate Forecast System Reanalysis (CFSR) ( Saha et al. 2010 ). There are, however, still substantial uncertainties and differences between these new reanalyses in poorly constrained quantities
agriculture. Knowledge of various snowpack properties is crucial for short-term weather forecasts, climate change prediction, and hydrologic forecasting for producing reliable daily to seasonal forecasts. One potential source of this information is the multi-institution North American Land Data Assimilation System (NLDAS) project ( Mitchell et al. 2004 ). Real-time NLDAS products are used for drought monitoring to support the National Integrated Drought Information System (NIDIS) and as initial conditions
agriculture. Knowledge of various snowpack properties is crucial for short-term weather forecasts, climate change prediction, and hydrologic forecasting for producing reliable daily to seasonal forecasts. One potential source of this information is the multi-institution North American Land Data Assimilation System (NLDAS) project ( Mitchell et al. 2004 ). Real-time NLDAS products are used for drought monitoring to support the National Integrated Drought Information System (NIDIS) and as initial conditions
