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Streamflow forecasting is critical to water resources management in the western United States. Yet, despite the passage of almost 50 years since the development of the first computerized hydrologic simulation models and over 30 years since the development of hydrologic ensemble forecast methods, the prevalent method used for forecasting seasonal streamflow in the western United States remains the regression of spring and summer streamflow volume on spring snowpack and/or the previous winter's accumulated precipitation. A recent retrospective analysis have shown that the skill of the regression-based forecasts have not improved in the last 40 years, despite large investments in science and technology related to the monitoring and assessment of the land surface and in climate forecasting. We describe an experimental streamflow forecast system for the western United States that applies a modern macroscale land surface model (akin to those now used in numerical weather prediction and climate models) to capture hydrologic states (soil moisture and snow) at the time of forecast, incorporates data assimilation methods to improve estimates of initial state, and uses a range of climate prediction ensembles to produce ensemble forecasts of streamflow and associated hydrologic states for lead times of up to one year. The forecast system is intended to be a real-time test bed for evaluating new seasonal streamflow forecast methods. Experience with the forecast system is illustrated using results from the 2004/05 forecast season, in which an evolving drought in the Pacific Northwest diverged strikingly from extreme snow accumulations to the south. We also discuss how the forecast system relates to ongoing changes in seasonal streamflow forecast methods in the two U.S. operational agencies that have major responsibility for seasonal streamflow forecasts in the western United States.
Streamflow forecasting is critical to water resources management in the western United States. Yet, despite the passage of almost 50 years since the development of the first computerized hydrologic simulation models and over 30 years since the development of hydrologic ensemble forecast methods, the prevalent method used for forecasting seasonal streamflow in the western United States remains the regression of spring and summer streamflow volume on spring snowpack and/or the previous winter's accumulated precipitation. A recent retrospective analysis have shown that the skill of the regression-based forecasts have not improved in the last 40 years, despite large investments in science and technology related to the monitoring and assessment of the land surface and in climate forecasting. We describe an experimental streamflow forecast system for the western United States that applies a modern macroscale land surface model (akin to those now used in numerical weather prediction and climate models) to capture hydrologic states (soil moisture and snow) at the time of forecast, incorporates data assimilation methods to improve estimates of initial state, and uses a range of climate prediction ensembles to produce ensemble forecasts of streamflow and associated hydrologic states for lead times of up to one year. The forecast system is intended to be a real-time test bed for evaluating new seasonal streamflow forecast methods. Experience with the forecast system is illustrated using results from the 2004/05 forecast season, in which an evolving drought in the Pacific Northwest diverged strikingly from extreme snow accumulations to the south. We also discuss how the forecast system relates to ongoing changes in seasonal streamflow forecast methods in the two U.S. operational agencies that have major responsibility for seasonal streamflow forecasts in the western United States.
Abstract
The use of probabilistic forecasts is necessary to take into account uncertainties and allow for optimal risk-based decisions in streamflow forecasting at monthly to seasonal lead times. Such probabilistic forecasts have long been used by practitioners in the operation of water reservoirs, in water allocation and management, and more recently in drought preparedness activities. Various studies assert the potential value of hydrometeorological forecasting efforts, but few investigate how these forecasts are used in the decision-making process. Role-playing games can help scientists, managers, and decision-makers understand the extremely complex process behind risk-based decisions. In this paper, we present an experiment focusing on the use of probabilistic forecasts to make decisions on reservoir outflows. The setup was a risk-based decision-making game, during which participants acted as water managers. Participants determined monthly reservoir releases based on a sequence of probabilistic inflow forecasts, reservoir volume objectives, and release constraints. After each decision, consequences were evaluated based on the actual inflow. The analysis of 162 game sheets collected after eight applications of the game illustrates the importance of leveraging not only the probabilistic information in the forecasts but also predictions for a range of lead times. Winning strategies tended to gradually empty the reservoir in the months before the peak inflow period to accommodate its volume and avoid overtopping. Twenty percent of the participants managed to do so and finished the management period without having exceeded the maximum reservoir capacity or violating downstream release constraints. The role-playing approach successfully created an open atmosphere to discuss the challenges of using probabilistic forecasts in sequential decision-making.
Abstract
The use of probabilistic forecasts is necessary to take into account uncertainties and allow for optimal risk-based decisions in streamflow forecasting at monthly to seasonal lead times. Such probabilistic forecasts have long been used by practitioners in the operation of water reservoirs, in water allocation and management, and more recently in drought preparedness activities. Various studies assert the potential value of hydrometeorological forecasting efforts, but few investigate how these forecasts are used in the decision-making process. Role-playing games can help scientists, managers, and decision-makers understand the extremely complex process behind risk-based decisions. In this paper, we present an experiment focusing on the use of probabilistic forecasts to make decisions on reservoir outflows. The setup was a risk-based decision-making game, during which participants acted as water managers. Participants determined monthly reservoir releases based on a sequence of probabilistic inflow forecasts, reservoir volume objectives, and release constraints. After each decision, consequences were evaluated based on the actual inflow. The analysis of 162 game sheets collected after eight applications of the game illustrates the importance of leveraging not only the probabilistic information in the forecasts but also predictions for a range of lead times. Winning strategies tended to gradually empty the reservoir in the months before the peak inflow period to accommodate its volume and avoid overtopping. Twenty percent of the participants managed to do so and finished the management period without having exceeded the maximum reservoir capacity or violating downstream release constraints. The role-playing approach successfully created an open atmosphere to discuss the challenges of using probabilistic forecasts in sequential decision-making.
Abstract
The February–March 2014 deployment of the National Aeronautics and Space Administration (NASA) Airborne Tropical Tropopause Experiment (ATTREX) provided unique in situ measurements in the western Pacific tropical tropopause layer (TTL). Six flights were conducted from Guam with the long-range, high-altitude, unmanned Global Hawk aircraft. The ATTREX Global Hawk payload provided measurements of water vapor, meteorological conditions, cloud properties, tracer and chemical radical concentrations, and radiative fluxes. The campaign was partially coincident with the Convective Transport of Active Species in the Tropics (CONTRAST) and the Coordinated Airborne Studies in the Tropics (CAST) airborne campaigns based in Guam using lower-altitude aircraft (see companion articles in this issue). The ATTREX dataset is being used for investigations of TTL cloud, transport, dynamical, and chemical processes, as well as for evaluation and improvement of global-model representations of TTL processes. The ATTREX data are publicly available online (at https://espoarchive.nasa.gov/).
Abstract
The February–March 2014 deployment of the National Aeronautics and Space Administration (NASA) Airborne Tropical Tropopause Experiment (ATTREX) provided unique in situ measurements in the western Pacific tropical tropopause layer (TTL). Six flights were conducted from Guam with the long-range, high-altitude, unmanned Global Hawk aircraft. The ATTREX Global Hawk payload provided measurements of water vapor, meteorological conditions, cloud properties, tracer and chemical radical concentrations, and radiative fluxes. The campaign was partially coincident with the Convective Transport of Active Species in the Tropics (CONTRAST) and the Coordinated Airborne Studies in the Tropics (CAST) airborne campaigns based in Guam using lower-altitude aircraft (see companion articles in this issue). The ATTREX dataset is being used for investigations of TTL cloud, transport, dynamical, and chemical processes, as well as for evaluation and improvement of global-model representations of TTL processes. The ATTREX data are publicly available online (at https://espoarchive.nasa.gov/).
A first-of-a-kind, extended-term cloud aircraft campaign was conducted to obtain an in situ statistical characterization of continental boundary layer clouds needed to investigate cloud processes and refine retrieval algorithms. Coordinated by the Atmospheric Radiation Measurement (ARM) Aerial Facility (AAF), the Routine AAF Clouds with Low Optical Water Depths (CLOWD) Optical Radiative Observations (RACORO) field campaign operated over the ARM Southern Great Plains (SGP) site from 22 January to 30 June 2009, collecting 260 h of data during 59 research flights. A comprehensive payload aboard the Center for Interdisciplinary Remotely-Piloted Aircraft Studies (CIRPAS) Twin Otter aircraft measured cloud microphysics, solar and thermal radiation, physical aerosol properties, and atmospheric state parameters. Proximity to the SGP's extensive complement of surface measurements provides ancillary data that support modeling studies and facilitates evaluation of a variety of surface retrieval algorithms. The five-month duration enabled sampling a range of conditions associated with the seasonal transition from winter to summer. Although about twothirds of the flights during which clouds were sampled occurred in May and June, boundary layer cloud fields were sampled under a variety of environmental and aerosol conditions, with about 77% of the cloud flights occurring in cumulus and stratocumulus. Preliminary analyses illustrate use of these data to analyze aerosol– cloud relationships, characterize the horizontal variability of cloud radiative impacts, and evaluate surface-based retrievals. We discuss how an extended-term campaign requires a simplified operating paradigm that is different from that used for typical, short-term, intensive aircraft field programs.
A first-of-a-kind, extended-term cloud aircraft campaign was conducted to obtain an in situ statistical characterization of continental boundary layer clouds needed to investigate cloud processes and refine retrieval algorithms. Coordinated by the Atmospheric Radiation Measurement (ARM) Aerial Facility (AAF), the Routine AAF Clouds with Low Optical Water Depths (CLOWD) Optical Radiative Observations (RACORO) field campaign operated over the ARM Southern Great Plains (SGP) site from 22 January to 30 June 2009, collecting 260 h of data during 59 research flights. A comprehensive payload aboard the Center for Interdisciplinary Remotely-Piloted Aircraft Studies (CIRPAS) Twin Otter aircraft measured cloud microphysics, solar and thermal radiation, physical aerosol properties, and atmospheric state parameters. Proximity to the SGP's extensive complement of surface measurements provides ancillary data that support modeling studies and facilitates evaluation of a variety of surface retrieval algorithms. The five-month duration enabled sampling a range of conditions associated with the seasonal transition from winter to summer. Although about twothirds of the flights during which clouds were sampled occurred in May and June, boundary layer cloud fields were sampled under a variety of environmental and aerosol conditions, with about 77% of the cloud flights occurring in cumulus and stratocumulus. Preliminary analyses illustrate use of these data to analyze aerosol– cloud relationships, characterize the horizontal variability of cloud radiative impacts, and evaluate surface-based retrievals. We discuss how an extended-term campaign requires a simplified operating paradigm that is different from that used for typical, short-term, intensive aircraft field programs.
