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  • 1
    Publication Date: 2022-10-26
    Description: © The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Subramanian, A. C., Balmaseda, M. A., Centurioni, L., Chattopadhyay, R., Cornuelle, B. D., DeMott, C., Flatau, M., Fujii, Y., Giglio, D., Gille, S. T., Hamill, T. M., Hendon, H., Hoteit, I., Kumar, A., Lee, J., Lucas, A. J., Mahadevan, A., Matsueda, M., Nam, S., Paturi, S., Penny, S. G., Rydbeck, A., Sun, R., Takaya, Y., Tandon, A., Todd, R. E., Vitart, F., Yuan, D., & Zhang, C. Ocean observations to improve our understanding, modeling, and forecasting of subseasonal-to-seasonal variability. Frontiers in Marine Science, 6, (2019): 427, doi:10.3389/fmars.2019.00427.
    Description: Subseasonal-to-seasonal (S2S) forecasts have the potential to provide advance information about weather and climate events. The high heat capacity of water means that the subsurface ocean stores and re-releases heat (and other properties) and is an important source of information for S2S forecasts. However, the subsurface ocean is challenging to observe, because it cannot be measured by satellite. Subsurface ocean observing systems relevant for understanding, modeling, and forecasting on S2S timescales will continue to evolve with the improvement in technological capabilities. The community must focus on designing and implementing low-cost, high-value surface and subsurface ocean observations, and developing forecasting system capable of extracting their observation potential in forecast applications. S2S forecasts will benefit significantly from higher spatio-temporal resolution data in regions that are sources of predictability on these timescales (coastal, tropical, and polar regions). While ENSO has been a driving force for the design of the current observing system, the subseasonal time scales present new observational requirements. Advanced observation technologies such as autonomous surface and subsurface profiling devices as well as satellites that observe the ocean-atmosphere interface simultaneously can lead to breakthroughs in coupled data assimilation (CDA) and coupled initialization for S2S forecasts. These observational platforms should also be tested and evaluated in ocean observation sensitivity experiments with current and future generation CDA and S2S prediction systems. Investments in the new ocean observations as well as model and DA system developments can lead to substantial returns on cost savings from disaster mitigation as well as socio–economic decisions that use S2S forecast information.
    Description: AS was funded by NOAA Climate Variability and Prediction Program (NA14OAR4310276) and the NSF Earth System Modeling Program (OCE1419306). CD was funded by NA16OAR4310094. SG and DG were funded by NASA awards NNX14AO78G and 80NSSC19K0059. DY was supported by NSFC (91858204, 41720104008, and 41421005).
    Keywords: Subseasonal ; Seasonal ; Predictions ; Air-sea interaction ; Satellite ; Argo ; Gliders ; Drifters
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 2
    Publication Date: 2016-01-01
    Description: The International Grand Global Ensemble (TIGGE) was a major component of The Observing System Research and Predictability Experiment (THORPEX) research program, whose aim is to accelerate improvements in forecasting high-impact weather. By providing ensemble prediction data from leading operational forecast centers, TIGGE has enhanced collaboration between the research and operational meteorological communities and enabled research studies on a wide range of topics. The paper covers the objective evaluation of the TIGGE data. For a range of forecast parameters, it is shown to be beneficial to combine ensembles from several data providers in a multimodel grand ensemble. Alternative methods to correct systematic errors, including the use of reforecast data, are also discussed. TIGGE data have been used for a range of research studies on predictability and dynamical processes. Tropical cyclones are the most destructive weather systems in the world and are a focus of multimodel ensemble research. Their extratropical transition also has a major impact on the skill of midlatitude forecasts. We also review how TIGGE has added to our understanding of the dynamics of extratropical cyclones and storm tracks. Although TIGGE is a research project, it has proved invaluable for the development of products for future operational forecasting. Examples include the forecasting of tropical cyclone tracks, heavy rainfall, strong winds, and flood prediction through coupling hydrological models to ensembles. Finally, the paper considers the legacy of TIGGE. We discuss the priorities and key issues in predictability and ensemble forecasting, including the new opportunities of convective-scale ensembles, links with ensemble data assimilation methods, and extension of the range of useful forecast skill.
    Print ISSN: 0003-0007
    Electronic ISSN: 1520-0477
    Topics: Geography , Physics
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  • 3
    Publication Date: 2016-10-10
    Description: Characteristics of precipitation estimates for rate and amount from three global high-resolution precipitation products (HRPPs), four global climate data records (CDRs), and four reanalyses are compared. All datasets considered have at least daily temporal resolution. Estimates of global precipitation differ widely from one product to the next, with some differences likely due to differing goals in producing the estimates. HRPPs are intended to produce the best snapshot of the precipitation estimate locally. CDRs of precipitation emphasize homogeneity over instantaneous accuracy. Precipitation estimates from global reanalyses are dynamically consistent with the large-scale circulation but tend to compare poorly to rain gauge estimates since they are forecast by the reanalysis system and precipitation is not assimilated. Regional differences among the estimates in the means and variances are as large as the means and variances, respectively. Even with similar monthly totals, precipitation rates vary significantly among the estimates. Temporal correlations among datasets are large at annual and daily time scales, suggesting that compensating bias errors at annual and random errors at daily time scales dominate the differences. However, the signal-to-noise ratio at intermediate (monthly) time scales can be large enough to result in high correlations overall. It is shown that differences on annual time scales and continental regions are around 0.8 mm day−1, which corresponds to 23 W m−2. These wide variations in the estimates, even for global averages, highlight the need for better constrained precipitation products in the future.
    Print ISSN: 0894-8755
    Electronic ISSN: 1520-0442
    Topics: Geography , Geosciences , Physics
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  • 4
    Publication Date: 2016-04-01
    Description: Ensemble postprocessing is frequently applied to correct biases and deficiencies in the spread of ensemble forecasts. Methods involving weighted, regression-corrected forecasts address the typical biases and underdispersion of ensembles through a regression correction of ensemble members followed by the generation of a probability density function (PDF) from the weighted sum of kernels fit around each corrected member. The weighting step accounts for the situation where the ensemble is constructed from different model forecasts or generated in some way that creates ensemble members that do not represent equally likely states. In the present work, it is shown that an overweighting of climatology in weighted, regression-corrected forecasts can occur when one first performs a regression-based correction before weighting each member. This overweighting of climatology results in an increase in the mean-squared error of the mean of the predicted PDF. The overweighting of climatology is illustrated in a simulation study and a real-data study, where the reference is generated through a direct application of Bayes’s rule. The real-data example is a comparison of a particular method referred to as Bayesian model averaging (BMA) and a direct application of Bayes’s rule for ocean wave heights using U.S. Navy and National Weather Service global deterministic forecasts. This direct application of Bayes’s rule is shown to not overweight climatology and may be a low-cost replacement for the generally more expensive weighted, regression-correction methods.
    Print ISSN: 0027-0644
    Electronic ISSN: 1520-0493
    Topics: Geography , Geosciences , Physics
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  • 5
    Publication Date: 2017-08-01
    Description: The U.S. National Blend of Models provides statistically postprocessed, high-resolution multimodel ensemble guidance, providing National Weather Service forecasters with a calibrated, downscaled starting point for producing digital forecasts. Forecasts of 12-hourly probability of precipitation (POP12) over the contiguous United States are produced as follows: 1) Populate the forecast and analyze cumulative distribution functions (CDFs) to be used later in quantile mapping. Were every grid point processed without benefit of data from other points, 60 days of training data would likely be insufficient for estimating CDFs and adjusting the errors in the forecast. Accordingly, “supplemental” locations were identified for each grid point, and data from the supplemental locations were used to populate the forecast and analyzed CDFs used in the quantile mapping. 2) Load the real-time U.S. and Environment Canada (now known as Environment and Climate Change Canada) global deterministic and ensemble forecasts, interpolated to ⅛°. 3) Using CDFs from the past 60 days of data, apply a deterministic quantile mapping to the ensemble forecasts. 4) Dress the resulting ensemble with random noise. 5) Generate probabilities from the ensemble relative frequency. 6) Spatially smooth the forecast using a Savitzky–Golay smoother, applying more smoothing in flatter areas. Forecasts of 6-hourly quantitative precipitation (QPF06) are more simply produced as follows: 1) Form a grand ensemble mean, again interpolated to ⅛°. 2) Quantile map the mean forecast using CDFs of the ensemble mean and analyzed distributions. 3) Spatially smooth the field, similar to POP12. Results for spring 2016 are provided, demonstrating that the postprocessing improves POP12 reliability and skill, as well as the deterministic forecast bias, while maintaining sharpness and spatial detail.
    Print ISSN: 0027-0644
    Electronic ISSN: 1520-0493
    Topics: Geography , Geosciences , Physics
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  • 6
    Publication Date: 2017-05-03
    Print ISSN: 0027-0644
    Electronic ISSN: 1520-0493
    Topics: Geography , Geosciences , Physics
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  • 7
    Publication Date: 2017-07-01
    Print ISSN: 0003-0007
    Electronic ISSN: 1520-0477
    Topics: Geography , Physics
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  • 8
    Publication Date: 2017-03-21
    Description: A Bayesian classification method for probabilistic forecasts of precipitation type is presented. The method considers the vertical wet-bulb temperature profiles associated with each precipitation type, transforms them into their principal components, and models each of these principal components by a skew normal distribution. A variance inflation technique is used to de-emphasize the impact of principal components corresponding to smaller eigenvalues, and Bayes’s theorem finally yields probability forecasts for each precipitation type based on predicted wet-bulb temperature profiles. This approach is demonstrated with reforecast data from the Global Ensemble Forecast System (GEFS) and observations at 551 METAR sites, using either the full ensemble or the control run only. In both cases, reliable probability forecasts for precipitation type being either rain, snow, ice pellets, freezing rain, or freezing drizzle are obtained. Compared to the model output statistics (MOS) approach presently used by the National Weather Service, the skill of the proposed method is comparable for rain and snow and significantly better for the freezing precipitation types.
    Print ISSN: 0027-0644
    Electronic ISSN: 1520-0493
    Topics: Geography , Geosciences , Physics
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  • 9
    Publication Date: 2017-01-01
    Print ISSN: 0027-0644
    Electronic ISSN: 1520-0493
    Topics: Geography , Geosciences , Physics
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  • 10
    Publication Date: 2019-03-01
    Description: Forecast uncertainty associated with the prediction of snowfall amounts is a complex superposition of the uncertainty about precipitation amounts and the uncertainty about weather variables like temperature that influence the snow-forming process. In situations with heavy precipitation, parametric, regression-based postprocessing approaches often perform very well since they can extrapolate relations between forecast and observed precipitation amounts established with data from more common events. The complexity of the relation between temperature and snowfall amounts, on the other hand, makes nonparametric techniques like the analog method an attractive choice. In this article we show how these two different methodologies can be combined in a way that leverages the respective advantages. Predictive distributions of precipitation amounts are obtained using a heteroscedastic regression approach based on censored, shifted gamma distributions, and quantile forecasts derived from them are used together with ensemble forecasts of temperature to find analog dates where both quantities were similar. The observed snowfall amounts on these dates are then used to compose an ensemble that represents the uncertainty about future snowfall. We demonstrate this approach with reforecast data from the Global Ensemble Forecast System (GEFS) and snowfall analyses from the National Operational Hydrologic Remote Sensing Center (NOHRSC) over an area within the northeastern United States and an area within the U.S. mountain states.
    Print ISSN: 0027-0644
    Electronic ISSN: 1520-0493
    Topics: Geography , Geosciences , Physics
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