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the illumination conditions and viewing geometry, and finally corrected for in-water bidirectional effects. The quantification of the accuracy of L WN , or of the equivalent remote sensing reflectance R RS , is essential to successively determine that affecting derived data products [e.g., chlorophyll-a concentration (Chla)]. For this reason, primary satellite radiometric products are matter of extensive validation programs aiming at verifying the compliance of their uncertainties with mission
the illumination conditions and viewing geometry, and finally corrected for in-water bidirectional effects. The quantification of the accuracy of L WN , or of the equivalent remote sensing reflectance R RS , is essential to successively determine that affecting derived data products [e.g., chlorophyll-a concentration (Chla)]. For this reason, primary satellite radiometric products are matter of extensive validation programs aiming at verifying the compliance of their uncertainties with mission
1. Introduction Most hydroclimatological processes, such as evaporation and precipitation, two critical components in forming the spatial pattern of water resources, are randomly included in nature so that accurate prediction of how they will change in the future is challenging and full of uncertainty. Although quantifying the uncertainty associated with the point predictions of these processes is essential, few studies have addressed these issues. Such phenomena are nonlinear and complex
1. Introduction Most hydroclimatological processes, such as evaporation and precipitation, two critical components in forming the spatial pattern of water resources, are randomly included in nature so that accurate prediction of how they will change in the future is challenging and full of uncertainty. Although quantifying the uncertainty associated with the point predictions of these processes is essential, few studies have addressed these issues. Such phenomena are nonlinear and complex
1. Introduction The significance of anthropogenic climate change in shaping and threatening the future of humankind, and the need for urgent political action is beyond scientific doubt—a fact that is supported by thousands of scholars worldwide and a large share of the policy-making community. Yet, the issue of uncertainty has been pervasive in the climate change discourse from the start ( Dessai et al. 2007 ). Workman et al. (2020) have in a recent work critically analyzed climate
1. Introduction The significance of anthropogenic climate change in shaping and threatening the future of humankind, and the need for urgent political action is beyond scientific doubt—a fact that is supported by thousands of scholars worldwide and a large share of the policy-making community. Yet, the issue of uncertainty has been pervasive in the climate change discourse from the start ( Dessai et al. 2007 ). Workman et al. (2020) have in a recent work critically analyzed climate
) showed, based on the latest phase 6 of CMIP (CMIP6) multimodels, that the estimated ToE of annual-mean SST is earlier than that of annual-mean rainfall, whereas the ToE of ENSO rainfall is earlier than that of ENSO SST. Although these qualitative findings are robust in the multimodel ensemble mean (MEM) result, as well as across most individual models, there remains considerable uncertainty concerning the exact ToEs for these signals. For example, the intermodel standard deviations for both the ToEs
) showed, based on the latest phase 6 of CMIP (CMIP6) multimodels, that the estimated ToE of annual-mean SST is earlier than that of annual-mean rainfall, whereas the ToE of ENSO rainfall is earlier than that of ENSO SST. Although these qualitative findings are robust in the multimodel ensemble mean (MEM) result, as well as across most individual models, there remains considerable uncertainty concerning the exact ToEs for these signals. For example, the intermodel standard deviations for both the ToEs
1. Introduction Observed meteorological forcing data used in land surface and hydrologic modeling contain large uncertainties ( Beven 2002 ; Carroll et al. 2006 ; Clark and Slater 2006 ; DeChant and Moradkhani 2012 ; Liu and Gupta 2007 ; Montanari and Koutsoyiannis 2012 ). Estimates of forcing data uncertainty can help to strengthen a myriad of land surface and hydrologic modeling and application practices. For instance, in hydrologic model calibration, uncertainty estimates in
1. Introduction Observed meteorological forcing data used in land surface and hydrologic modeling contain large uncertainties ( Beven 2002 ; Carroll et al. 2006 ; Clark and Slater 2006 ; DeChant and Moradkhani 2012 ; Liu and Gupta 2007 ; Montanari and Koutsoyiannis 2012 ). Estimates of forcing data uncertainty can help to strengthen a myriad of land surface and hydrologic modeling and application practices. For instance, in hydrologic model calibration, uncertainty estimates in
) currently communicates tornado risk to the public is by issuing either a tornado watch or a tornado warning. A “watch” means tornadoes are possible in and near the designated area. A “warning” means that a tornado is imminent or occurring and taking shelter is advised ( NWS 2012 ). a. Forecast uncertainty Although the current warning system fails to acknowledge it directly, meteorologists know that the probability of a tornado varies geographically within the warned area and changes over time
) currently communicates tornado risk to the public is by issuing either a tornado watch or a tornado warning. A “watch” means tornadoes are possible in and near the designated area. A “warning” means that a tornado is imminent or occurring and taking shelter is advised ( NWS 2012 ). a. Forecast uncertainty Although the current warning system fails to acknowledge it directly, meteorologists know that the probability of a tornado varies geographically within the warned area and changes over time
et al. 2013 ; Jha and Gassman 2014 ; Kay et al. 2009 ; Lu et al. 2018 ; Maurer and Duffy 2005 ; Stephens et al. 2018 ; Wu et al. 2014 , 2015 ; Xu et al. 2013 ; Zhang et al. 2011 ). Uncertainty is inevitable and important in numerical models, especially complex hydrological models of future climate change impacts ( Kay et al. 2009 ; Neuman 2003 ; Refsgaard et al. 2007 ). Uncertainties arise from variant sources, including unpredictable future conditions, lack of knowledge or data for
et al. 2013 ; Jha and Gassman 2014 ; Kay et al. 2009 ; Lu et al. 2018 ; Maurer and Duffy 2005 ; Stephens et al. 2018 ; Wu et al. 2014 , 2015 ; Xu et al. 2013 ; Zhang et al. 2011 ). Uncertainty is inevitable and important in numerical models, especially complex hydrological models of future climate change impacts ( Kay et al. 2009 ; Neuman 2003 ; Refsgaard et al. 2007 ). Uncertainties arise from variant sources, including unpredictable future conditions, lack of knowledge or data for
). Sujono et al. ( Sujono et al. 2004 ) indicated that a more objective method of wavelet transform appears promising. Once properly parameterized, automated methods are widely accepted to be more objective and reproducible ( Nathan and McMahon 1990 ; Arnold and Allen 1999 ; Furey and Gupta 2001 ) and therefore preferable over more subjective graphical methods. In addition to algorithm structure, the timing and magnitude of precipitation is a key determinant and uncertainty in evaluating hydrologic
). Sujono et al. ( Sujono et al. 2004 ) indicated that a more objective method of wavelet transform appears promising. Once properly parameterized, automated methods are widely accepted to be more objective and reproducible ( Nathan and McMahon 1990 ; Arnold and Allen 1999 ; Furey and Gupta 2001 ) and therefore preferable over more subjective graphical methods. In addition to algorithm structure, the timing and magnitude of precipitation is a key determinant and uncertainty in evaluating hydrologic
al. 2004 ), GSMaP ( Okamoto et al. 2005 ), IMERG ( Huffman et al. 2020 ), and others that aim to produce precipitation with the highest space–time resolution available but at the expense of consistency in the long-term time series. A number of efforts have been undertaken to establish the accuracy of the products listed above by comparing them with each other as well as with the global model reanalysis. It was through one of these efforts ( Adler et al. 2012 ) that GPCP estimated uncertainties
al. 2004 ), GSMaP ( Okamoto et al. 2005 ), IMERG ( Huffman et al. 2020 ), and others that aim to produce precipitation with the highest space–time resolution available but at the expense of consistency in the long-term time series. A number of efforts have been undertaken to establish the accuracy of the products listed above by comparing them with each other as well as with the global model reanalysis. It was through one of these efforts ( Adler et al. 2012 ) that GPCP estimated uncertainties
the skill of intensity guidance has increased due to improvements in the numerical weather prediction (NWP) models stemming from the Hurricane Forecast Improvement Project (HFIP; Gall et al. 2013 ), consensus aids, rapid intensification (RI) aids, and the ability of forecasters to synthesize the data. In addition to improving hurricane track and intensity forecasts, estimates of uncertainty of those parameters also needs to be improved in order to communicate risk ( Marks et al. 2019 ). The
the skill of intensity guidance has increased due to improvements in the numerical weather prediction (NWP) models stemming from the Hurricane Forecast Improvement Project (HFIP; Gall et al. 2013 ), consensus aids, rapid intensification (RI) aids, and the ability of forecasters to synthesize the data. In addition to improving hurricane track and intensity forecasts, estimates of uncertainty of those parameters also needs to be improved in order to communicate risk ( Marks et al. 2019 ). The
