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Mark S. Allen and F. Anthony Eckel
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Mark S. Allen and F. Anthony Eckel

Abstract

This study explores the objective application of ambiguity information, that is, the uncertainty in forecast probability derived from an ensemble. One application approach, called uncertainty folding, merges ambiguity with forecast uncertainty information for subsequent use in standard risk-analysis decision making. Uncertainty folding is found to be of no practical benefit when tested in a low-order, weather forecast simulation. A second approach, called ulterior motives, attempts to use ambiguity information to aid secondary decision factors not considered in the standard risk analysis, while simultaneously maintaining the primary value associated with the probabilistic forecasts. Following ulterior motives, the practical utility of ambiguity information is demonstrated on real-world ensemble forecasts used to support decisions concerning the preparation for freezing temperatures paired with a secondary desire for the reduction in repeat false alarms. Sample products for communicating ambiguity to the user are also presented.

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F. Anthony Eckel, Mark S. Allen, and Matthew C. Sittel

Abstract

Ambiguity is uncertainty in the prediction of forecast uncertainty, or in the forecast probability of a specific event, associated with random error in an ensemble forecast probability density function. In ensemble forecasting ambiguity arises from finite sampling and deficient simulation of the various sources of forecast uncertainty. This study introduces two practical methods of estimating ambiguity and demonstrates them on 5-day, 2-m temperature forecasts from the Japan Meteorological Agency’s Ensemble Prediction System. The first method uses the error characteristics of the calibrated ensemble as well as the ensemble spread to predict likely errors in forecast probability. The second method applies bootstrap resampling on the ensemble members to produce multiple likely values of forecast probability. Both methods include forecast calibration since ambiguity results from random and not systematic errors, which must be removed to reveal the ambiguity. Additionally, use of a more robust calibration technique (improving beyond just correcting average errors) is shown to reduce ambiguity. Validation using a low-order dynamical system reveals that both estimation methods have deficiencies but exhibit some skill, making them candidates for application to decision making—the subject of a companion paper.

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F. Anthony Eckel, Mark S. Allen, and Matthew C. Sittel
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Hung-Neng S. Chin, Daniel J. Rodriguez, Richard T. Cederwall, Catherine C. Chuang, Allen S. Grossman, John J. Yio, Qiang Fu, and Mark A. Miller

Abstract

Using measurements from the Department of Energy’s Atmospheric Radiation Measurement Program, a modified ground-based remote sensing technique is developed and evaluated to study the impacts of the subadiabatic character of continental low-level stratiform clouds on microphysical properties and radiation budgets. Airborne measurements and millimeter-wavelength cloud radar data are used to validate retrieved microphysical properties of three stratus cloud systems occurring in the April 1994 and 1997 intensive observation periods at the Southern Great Plains site.

The addition of the observed cloud-top height into the Han and Westwater retrieval scheme eliminates the need to invoke the adiabatic assumption. Thus, the retrieved liquid water content (LWC) profile is represented as the product of an adiabatic LWC profile and a weighting function. Based on in situ measurements, two types of weighting functions are considered in this study: one is associated with a subadiabatic condition involving cloud-top entrainment mixing alone (type I) and the other accounts for both cloud-top entrainment mixing and drizzle effects (type II). The adiabatic cloud depth ratio (ACDR), defined as the ratio of the actual cloud depth to the one derived from the adiabatic assumption, is found to be a useful parameter for classifying the subadiabatic character of low-level stratiform clouds. The type I weighting function only exists in the lower ACDR regime, while the type II profile can appear for any adiabatic cloud depth ratio.

Results indicate that the subadiabatic character of low-level stratiform clouds has substantial impacts on radiative energy budgets, especially those in the shortwave, via the retrieved LWC distribution and its related effective radius profile of liquid water. Results also show that this subadiabatic character can act to stabilize the cloud deck by reducing the in-cloud radiative heating/cooling contrast. As a whole, these impacts strengthen as the subadiabatic character of low-level stratiform clouds increases.

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Richard Rotunno, Leonard J. Pietrafesa, John S. Allen, Bradley R. Colman, Clive M. Dorman, Carl W. Kreitzberg, Stephen J. Lord, Miles G. McPhee, George L. Mellor, Christopher N. K. Mooers, Pearn P. Niiler, Roger A. Pielke Sr., Mark D. Powell, David P. Rogers, James D. Smith, and Lian Xie

U.S. Weather Research Program (USWRP) prospectus development teams (PDTs) are small groups of scientists that are convened by the USWRP lead scientist on a one-time basis to discuss critical issues and to provide advice related to future directions of the program. PDTs are a principal source of information for the Science Advisory Committee, which is a standing committee charged with the duty of making recommendations to the Program Office based upon overall program objectives. PDT-1 focused on theoretical issues, and PDT-2 on observational issues; PDT-3 is the first of several to focus on more specialized topics. PDT-3 was convened to identify forecasting problems related to U.S. coastal weather and oceanic conditions, and to suggest likely solution strategies.

There were several overriding themes that emerged from the discussion. First, the lack of data in and over critical regions of the ocean, particularly in the atmospheric boundary layer, and the upper-ocean mixed layer were identified as major impediments to coastal weather prediction. Strategies for data collection and dissemination, as well as new instrument implementation, were discussed. Second, fundamental knowledge of air–sea fluxes and boundary layer structure in situations where there is significant mesoscale variability in the atmosphere and ocean is needed. Companion field studies and numerical prediction experiments were discussed. Third, research prognostic models suggest that future operational forecast models pertaining to coastal weather will be high resolution and site specific, and will properly treat effects of local coastal geography, orography, and ocean state. The view was expressed that the exploration of coupled air-sea models of the coastal zone would be a particularly fruitful area of research. PDT-3 felt that forecasts of land-impacting tropical cyclones, Great Lakes-affected weather, and coastal cyclogenesis, in particular, would benefit from such coordinated modeling and field efforts. Fourth, forecasting for Arctic coastal zones is limited by our understanding of how sea ice forms. The importance of understanding air-sea fluxes and boundary layers in the presence of ice formation was discussed. Finally, coastal flash flood forecasting via hydrologic models is limited by the present accuracy of measured and predicted precipitation and storm surge events. Strategies for better ways to improve the latter were discussed.

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James D. Doyle, Jonathan R. Moskaitis, Joel W. Feldmeier, Ronald J. Ferek, Mark Beaubien, Michael M. Bell, Daniel L. Cecil, Robert L. Creasey, Patrick Duran, Russell L. Elsberry, William A. Komaromi, John Molinari, David R. Ryglicki, Daniel P. Stern, Christopher S. Velden, Xuguang Wang, Todd Allen, Bradford S. Barrett, Peter G. Black, Jason P. Dunion, Kerry A. Emanuel, Patrick A. Harr, Lee Harrison, Eric A. Hendricks, Derrick Herndon, William Q. Jeffries, Sharanya J. Majumdar, James A. Moore, Zhaoxia Pu, Robert F. Rogers, Elizabeth R. Sanabia, Gregory J. Tripoli, and Da-Lin Zhang

Abstract

Tropical cyclone (TC) outflow and its relationship to TC intensity change and structure were investigated in the Office of Naval Research Tropical Cyclone Intensity (TCI) field program during 2015 using dropsondes deployed from the innovative new High-Definition Sounding System (HDSS) and remotely sensed observations from the Hurricane Imaging Radiometer (HIRAD), both on board the NASA WB-57 that flew in the lower stratosphere. Three noteworthy hurricanes were intensively observed with unprecedented horizontal resolution: Joaquin in the Atlantic and Marty and Patricia in the eastern North Pacific. Nearly 800 dropsondes were deployed from the WB-57 flight level of ∼60,000 ft (∼18 km), recording atmospheric conditions from the lower stratosphere to the surface, while HIRAD measured the surface winds in a 50-km-wide swath with a horizontal resolution of 2 km. Dropsonde transects with 4–10-km spacing through the inner cores of Hurricanes Patricia, Joaquin, and Marty depict the large horizontal and vertical gradients in winds and thermodynamic properties. An innovative technique utilizing GPS positions of the HDSS reveals the vortex tilt in detail not possible before. In four TCI flights over Joaquin, systematic measurements of a major hurricane’s outflow layer were made at high spatial resolution for the first time. Dropsondes deployed at 4-km intervals as the WB-57 flew over the center of Hurricane Patricia reveal in unprecedented detail the inner-core structure and upper-tropospheric outflow associated with this historic hurricane. Analyses and numerical modeling studies are in progress to understand and predict the complex factors that influenced Joaquin’s and Patricia’s unusual intensity changes.

Open access
James M. Wilczak, Mark Stoelinga, Larry K. Berg, Justin Sharp, Caroline Draxl, Katherine McCaffrey, Robert M. Banta, Laura Bianco, Irina Djalalova, Julie K. Lundquist, Paytsar Muradyan, Aditya Choukulkar, Laura Leo, Timothy Bonin, Yelena Pichugina, Richard Eckman, Charles N. Long, Kathleen Lantz, Rochelle P. Worsnop, Jim Bickford, Nicola Bodini, Duli Chand, Andrew Clifton, Joel Cline, David R. Cook, Harindra J. S. Fernando, Katja Friedrich, Raghavendra Krishnamurthy, Melinda Marquis, Jim McCaa, Joseph B. Olson, Sebastian Otarola-Bustos, George Scott, William J. Shaw, Sonia Wharton, and Allen B. White

Abstract

The Second Wind Forecast Improvement Project (WFIP2) is a U.S. Department of Energy (DOE)- and National Oceanic and Atmospheric Administration (NOAA)-funded program, with private-sector and university partners, which aims to improve the accuracy of numerical weather prediction (NWP) model forecasts of wind speed in complex terrain for wind energy applications. A core component of WFIP2 was an 18-month field campaign that took place in the U.S. Pacific Northwest between October 2015 and March 2017. A large suite of instrumentation was deployed in a series of telescoping arrays, ranging from 500 km across to a densely instrumented 2 km × 2 km area similar in size to a high-resolution NWP model grid cell. Observations from these instruments are being used to improve our understanding of the meteorological phenomena that affect wind energy production in complex terrain and to evaluate and improve model physical parameterization schemes. We present several brief case studies using these observations to describe phenomena that are routinely difficult to forecast, including wintertime cold pools, diurnally driven gap flows, and mountain waves/wakes. Observing system and data product improvements developed during WFIP2 are also described.

Open access