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A Nonparametric Ensemble Postprocessing Approach for Short-Range Visibility Predictions in Data-Sparse Areas

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A Nonparametric Ensemble Postprocessing Approach for Short-Range Visibility Predictions in Data-Sparse Areas JUNE 2018 R Y E R S O N A N D H A C K E R 835 A Nonparametric Ensemble Postprocessing Approach for Short-Range Visibility Predictions in Data-Sparse Areas a b WILLIAM R. RYERSON AND JOSHUA P. HACKER Naval Postgraduate School, Department of Meteorology, Monterey, California (Manuscript received 2 July 2017, in final form 9 December 2017) ABSTRACT This work develops and tests the viability of obtaining skillful short-range (,20 h) visibility predictions using statistical postprocessing of a 4-km, 10-member Weather Research and Forecasting (WRF) ensemble configured to closely match the U.S. Air Force Mesoscale Ensemble Forecast System. The raw WRF pre- dictions produce excessive forecasts of zero cloud water, which is simultaneously predicted by all ensemble members in 62% of observed fog cases, leading to zero ensemble dispersion and no skill in these cases. Adding dispersion to the clear cases by making upward adjustments to cloud water predictions from individual members not predicting fog on their own provides the best chance to increase the resolution and reliability of the ensemble. The technique leverages traits of a joint parameter space in the predictions and is generally most effective when the space is defined with a moisture parameter and a low-level stability parameter. Cross- validation shows that the method adds significant overnight skill to predictions in valley and coastal regions compared to the raw WRF forecasts, with modest skill increases after sunrise. Postprocessing does not im- prove the highly skillful raw WRF predictions at the mountain test sites. Since the framework addresses only systematic WRF deficiencies and identifies parameter pairs with a clear, non-site-specific physical mechanism of predictive power, it has geographical transferability with less need for recalibration or observational record compared to other statistical postprocessing approaches. 1. Introduction National Ceiling and Visibility Analysis product, which uses a decision-tree framework to assimilate real-time Light fog, defined here as fog resulting in visibility of surface and satellite observations with model data to 1–7 mi, occurs nearly globally and is a significant safety make ceiling and visibility predictions to 12 h (Herzegh concern for many aviation operations. In the Depart- et al. 2006). ment of Defense, light fog can drive restrictions on A statistical link between a numerical forecast and the certain aircraft types, equipment, and less-experienced expected error in fog prediction can be drawn, for ex- pilots such that the ability to conduct operations is se- ample analogous to MOS, but the predictors cannot be verely degraded. Postprocessing numerical forecasts chosen blindly, and the relationships can be highly with observations may mitigate the lack of accuracy in nonlinear. This is the reason for the use of, for example, predicting light fog. Where observational records are nonlinear neural networks in fog prediction (Marzban unavailable, standard statistical corrections that may et al. 2007). not consider the physical state of the model are im- We propose a nonparametric approach to ensemble possible. The lack of observations in these locales also fog prediction, where the predictand is the probability of rules out robust tools available in data-rich re- light fog. Ryerson and Hacker (2014, hereafter RH14) gions, such as the Federal Aviation Administration’s examined raw predictions from an uncalibrated 4-km, 10-member Weather Research and Forecasting (WRF) a Current affiliation: U.S. Air Force, Offutt Air Force Base, Model (Skamarock et al. 2008) ensemble based on Nebraska. varying physical parameterizations. Analysis showed b Current affiliation: Jupiter, Boulder, Colorado. the raw (i.e., uncalibrated) predictions from the en- semble produce a severe shortage of light fog pre- Corresponding author: William R. Ryerson, william.ryerson@ dictions corresponding to visibilities of 1–7 mi, in favor us.af.mil of excessive forecasts of zero cloud water (no fog). The DOI: 10.1175/WAF-D-17-0066.1 Ó 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). Unauthenticated | Downloaded 09/30/21 03:26 AM UTC 836 WEATHER AND FORECASTING VOLUME 33 raw predictions had skillful results in a mountainous version of the Unified Model with a modified cloud region (the Sierra Nevada near the California–Nevada parameterization, Boutle et al. (2016) achieved perhaps border), mixed results in a coastal region (the Pacific the most promising results to date but observed little coast in northern California), and were not skillful in a improvement at shorter forecast lengths, and computa- valley region (the California Central Valley) dominated tional expense currently limits a model domain of this by radiation fog. Error in predictions of cloud water resolution to perhaps a single state or province (i.e., mixing ratio qc was primarily due to systematic NWP 100 km 3 100 km) in real-time operations. Given these physics error, defined broadly as error resulting from an limitations, a common approach is to rely on a more inaccurate parameterization of subgrid-scale processes sophisticated visibility parameterization that leverages in the model ultimately leading to error in resolved NWP output variables beyond just qc. variables. Examples of these more sophisticated visibility pa- Most previous work in fog prediction using NWP and rameterizations are plentiful in both operations and re- postprocessing has focused on heavy fog (for simplicity search. Until recently, visibility predictions from the U.S. we define heavy fog as conditions that reduce visibility Air Force’s Mesoscale Ensemble Prediction System to ,1 mi), but error from subgrid-scale parameteriza- (MEPS) used a statistical parameterization developed tions is a consistent theme. In a thorough review of the from regression on a 1-yr training dataset of RUC ana- literature and a comparison of several fog NWP models, lyses at thousands of U.S. locations (E. Kuchera 2011, Gultepe et al. (2007a) emphasized several challenges to personal communication), with predictors of total column accurately predicting fog with an NWP model, notably precipitable water, 10-m wind speed, and 2-m relative the need for very high horizontal resolution that is humidity (RH). Zhou and Du (2010) also applied a ‘‘sufficiently less than a few meters’’ as well as the need unique visibility parameterization to output from a 15-km to account for horizontal heterogeneities of soil and resolution, 10-member ensemble, in order to make a yes/ vegetation. no radiation fog prediction based on qc,10-mwindspeed, Geiszler et al. (2000) offered an informative example 2-m RH, and cloud-top and cloud-base heights. of attempting fog prediction using direct, uncalibrated Since these more complex visibility parameterizations NWP output of qc. Coupled with a simple visibility pa- are trained using observational predictors, they effec- rameterization to convert NWP predictions of qc to tively improve the quality of the parameterization while visibility, they tested a 9-km-resolution version of the remaining susceptible to error in the NWP predictions Coupled Ocean–Atmospheric Mesoscale Prediction of the input parameters. Yet RH14 showed that error in System (COAMPS) model over coastal California, the NWP predictions of parameters most germane for finding the results had little skill. The somewhat more fog (i.e., qc, temperature, and water vapor) are the most skillful results attained by direct, uncalibrated qc pre- important source of error, suggesting a limitation on the dictions in RH14 were from stochastic verification of an capability of parameterization strategies built from ob- ensemble; each individual ensemble member was un- servational data that ignore NWP error. skillful on its own. Similarly, Zhou et al. (2009) obtained In a unique approach, Zhou and Ferrier (2008) qualitatively better results than Geiszler et al. (2000) described a physically based computational method for when applying the same simple visibility parameteriza- obtaining qc values during radiation fog events by ex- tion to NWP qc predictions from the 32-km horizontal plicitly solving the governing equation that diagnoses qc resolution, 21-member Short Range Ensemble Forecast as a function of the turbulent exchange coefficient, system produced by the National Centers for Environ- droplet gravitational settling, condensation rate due to mental Predictions (NCEP). Yet in a recent prolonged cooling, and depth of the fog layer all taken from nu- case of widespread fog in the United Kingdom, Price merical predictions. The abundance of zero or near-zero et al. (2015) found that 1.5-km ensemble predictions qc predictions from the 10-member ensemble docu- from a high-resolution version of the U.K. Met Office mented in RH14 eliminate fog depth as a useful input for Unified Model struggled to distinguish low stratus cloud diagnosis. Zhou (2011) suggested that for known nega- from fog and predicted premature dissipation. tive RH biases, defining the fog depth as the depth These results support the fundamental advantage of where an RH threshold is exceeded in the NWP pre- an ensemble, but also suggest that the prospects of dictions can help. attaining consistently skillful fog prediction using direct, The next section will discuss the basis and methodol- uncalibrated NWP predictions of qc are limited using a ogy for the postprocessing strategy used in this work, horizontal resolution of a few kilometers or more, cur- followed by the process of optimizing the technique in rently the approximate limit for most operational NWP section 3. Cross validation is presented in section 4, and modeling centers. Even at a resolution of 333 m, using a section 5 offers a summary and conclusions. Unauthenticated | Downloaded 09/30/21 03:26 AM UTC JUNE 2018 R Y E R S O N A N D H A C K E R 837 TABLE 1. ASOS daytime and nighttime algorithms for converting 62% of all observed fog cases, which results in zero en- b the extinction coefficient e to visibility rt.
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