ALBERT

Description: The year 1919 was important in meteorology, not only because it was the year that the American Meteorological Society was founded, but also for two other reasons. One of the foundational papers in extratropical cyclone structure by Jakob Bjerknes was published in 1919, leading to what is now known as the Norwegian cyclone model. Also that year, a series of meetings was held that led to the formation of organizations that promoted the international collaboration and scientific exchange required for extratropical cyclone research, which by necessity involves spatial scales spanning national borders. This chapter describes the history of scientific inquiry into the structure, evolution, and dynamics of extratropical cyclones, their constituent fronts, and their attendant jet streams and storm tracks. We refer to these phenomena collectively as the centerpiece of meteorology because of their central role in fostering meteorological research during this century. This extremely productive period in extratropical cyclone research has been possible because of 1) the need to address practical challenges of poor forecasts that had large socioeconomic consequences, 2) the intermingling of theory, observations, and diagnosis (including dynamical modeling) to provide improved physical understanding and conceptual models, and 3) strong international cooperation. Conceptual frameworks for cyclones arise from a desire to classify and understand cyclones; they include the Norwegian cyclone model and its sister the Shapiro–Keyser cyclone model. The challenge of understanding the dynamics of cyclones led to such theoretical frameworks as quasigeostrophy, baroclinic instability, semigeostrophy, and frontogenesis. The challenge of predicting explosive extratropical cyclones in particular led to new theoretical developments such as potential-vorticity thinking and downstream development. Deeper appreciation of the limits of predictability has resulted from an evolution from determinism to chaos. Last, observational insights led to detailed cyclone and frontal structure, storm tracks, and rainbands.

Description: This paper explores simulated changes to the cool-season (November–March) storm-surge and coastal-flooding events at the Battery in New York City, New York (NYC), during most of the twenty-first century using several climate models and a previously developed multilinear regression model. The surface wind and pressure forcing for the surge predictions are obtained from an ensemble of 6 coupled global climate models (GCM) and 30 members from the Community Earth System Model. Using the “RCP8.5” emission scenario, both the single-model and multimodel ensemble means yielded insignificant (significance level p 〉 0.05) simulated changes to the median surge event (〉0.61 m above astronomical tide) between a historical period (1979–2004) and the mid-to-late twenty-first century (2054–79). There is also little change in the return interval for the moderate-to-high surge events. By the mid-to-late twenty-first century, there is a poleward shift of the mean surface cyclone track in many of the models and most GCMs demonstrate an intensification of the average cyclone. There is little effect on the future surge events at the Battery because most of these storm changes are not in a region that favors more or higher-amplitude surges at NYC. Rather, projected sea level rise dominates the future simulated change in the number of flooding events by the mid-to-late twenty-first century. For example, the projections show about 23 times as many coastal-flooding events (tide + surge ≥ 2.44 m above mean lower low water; 1983–2001) in 2079 when compared with 1979, and the return intervals for some major coastal floods (e.g., the December 1992 northeaster) decrease by a factor of 3–4.

Description: The Short-Range Ensemble Forecast (SREF) system is verified and bias corrected for fire weather days (FWDs) defined as having an elevated probability of wildfire occurrence using a statistical Fire Weather Index (FWI) over a subdomain of the northeastern United States (NEUS) between 2007 and 2014. The SREF is compared to the Rapid Update Cycle and Rapid Refresh analyses for temperature, relative humidity, specific humidity, and the FWI. An additive bias correction is employed using the most recent previous 14 days [sequential bias correction (SBC)] and the most recent previous 14 FWDs [conditional bias correction (CBC)]. Synoptic weather regimes on FWDs are established using cluster analysis (CA) on North American Regional Reanalysis sea level pressure, 850-hPa temperature, 500-hPa temperature, and 500-hPa geopotential height. SREF severely underpredicts FWI (by two indices at FWI = 3) on FWDs, which is partially corrected using SBC and largely corrected with CBC. FWI underprediction is associated with a cool (ensemble mean error of −1.8 K) and wet near-surface model bias (ensemble mean error of 0.46 g kg−1) that decreases to near zero above 800 hPa. Although CBC improves reliability and Brier skill scores on FWDs, ensemble FWI values exhibit underdispersion. CA reveals three synoptic weather regimes on FWDs, with the largest cool and wet biases associated with a departing surface low pressure system. These results suggest the potential benefit of an operational analog bias correction on FWDs. Furthermore, CA may help elucidate model error during certain synoptic weather regimes.

Description: The wind resource offshore of the East Coast of the United States is well known for its potential to provide abundant, clean, renewable, and domestic electricity. However, limited observations from this region are recorded at heights above the water that penetrate significantly into the planetary boundary layer (PBL). As a result, mesoscale models have been used to characterize the offshore wind resource in this region but have not been evaluated fully within the PBL due to the scarcity of observations. This paper describes the setup and some early results from the Improving the Mapping and Prediction of Offshore Wind Resources (IMPOWR) field study conducted in the Nantucket Sound area in 2013/14. The IMPOWR campaign provides a rich dataset of observations within the PBL from a variety of sources: high-frequency Long-EZ aircraft, a multilevel atmospheric and oceanic tower in Nantucket Sound, and lidars on the south shore of eastern Long Island and Block Island. In addition to new data for model validation and wind resource assessment, the IMPOWR field campaign provides new insights on meteorological features important for wind power development, such as the New York Bight jet and shallow marine layer.

Description: A fire weather index (FWI) is developed using wildfire occurrence data and Automated Surface Observing System weather observations within a subregion of the northeastern United States (NEUS) from 1999 to 2008. Average values of several meteorological variables, including near-surface temperature, relative humidity, dewpoint, wind speed, and cumulative daily precipitation, are compared on observed wildfire days with their climatological average (“climatology”) using a bootstrap resampling approach. Average daily minimum relative humidity is significantly lower than climatology on wildfire occurrence days, and average daily maximum temperature and average daily maximum wind speed are slightly higher on wildfire occurrence days. Using the potentially important weather variables (relative humidity, temperature, and wind speed) as inputs, different formulations of a binomial logistic regression model are tested to assess the potential of these atmospheric variables for diagnosing the probability of wildfire occurrence. The FWI is defined using probabilistic output from the preferred binomial logistic regression configuration. Relative humidity and temperature are the only significant predictors in the binomial logistic regression. The binomial logistic regression model is reliable and has more probabilistic skill than climatology using an independent verification dataset. Using the binomial logistic regression output probabilities, an FWI is developed ranging from 0 (minimum potential) to 3 (high potential) and is verified independently for two separate subdomains within the NEUS. The climatology of the FWI reproduces observed fire occurrence probabilities between 1999 and 2008 over a subdomain of the NEUS.

Description: Future changes in the frequency of environmental conditions conducive for convective storm days (“CE days”) are determined for the northeastern United States (NEUS) during the warm seasons (April–September) of the twenty-first century. Statistical relationships between historical runs of seven models in phase 5 of the Coupled Model Intercomparison Project (CMIP5) and radar-classified convective storm days are developed using linear discriminant analysis (LDA), and these relationships are then applied to analyze changes in the convective environment under the high-emissions representative concentration pathway 8.5 (RCP8.5) scenario over the period 2006–99. The 1996–2007 warm seasons are used to train the LDA thresholds using convective precipitation from two reanalysis datasets and radar data, and the 1979–95 and 2008–10 warm seasons are used to verify these thresholds. For the CMIP5 historical period (1979–2005), the frequency of warm season CE days averaged across the CMIP5 models is slightly greater than that derived using reanalysis data, although both methods indicate a slight increasing trend through the historical period. Between 2006 and 2099, warm season CE day frequency is predicted to increase substantially at an average rate of 4–5 days decade−1 (50%–80% increase over the entire period). These changes are mostly attributed to a predicted 30%–40% increase in midlevel precipitable water between the historical period and the last few decades of the twenty-first century. Consistent with previous studies, there is decreasing deep-layer vertical wind shear as a result of a weakening horizontal temperature gradient, but this is outweighed by increases in instability led by the moisture increases.

Description: Derived radar reflectivities and fall speeds for four Weather Research and Forecasting (WRF) Model bulk microphysical parameterizations (BMPs) run at 1.33-km grid spacing are compared with ground-based, vertically pointing Ku-band radar, scanning S-band radar, and in situ measurements at Stony Brook, New York. Simulations were partitioned into periods of observed riming degree as determined manually using a stereo microscope and camera during nine winter storms. Simulations were examined to determine whether the selected BMPs captured the effects of varying riming intensities, provided a reasonable match to the vertical structure of radar reflectivity or fall speed, and whether they produced reasonable surface fall speed distributions. Schemes assuming nonspherical mass–diameter relationships yielded reflectivity distributions closer to observed values. All four schemes examined in this study provided a better match to the observed, vertical structure of reflectivity during moderate riming than light riming periods. The comparison of observed and simulated snowfall speeds had mixed results. One BMP produced episodes of excessive cloud water at times, resulting in fall speeds that were too large. However, most schemes had frequent periods of little or no cloud water during moderate riming periods and thus underpredicted the snowfall speeds at lower levels. Short, 1–4-h periods with relatively steady snow conditions were used to compare BMP and observed size and fall speed distributions. These limited data suggest the examined BMPs underpredict fall speeds of cold-type snow habits and underrepresent aggregates larger than 4-mm diameter.

Description: This paper evaluates the extratropical cyclones within three operational global ensembles [the 20-member Canadian Meteorological Centre (CMC), 20-member National Centers for Environmental Prediction (NCEP), and 50-member European Centre for Medium-Range Weather Forecasts (ECMWF)]. The day-0–6 forecasts were evaluated over the eastern United States and western Atlantic for the 2007–15 cool seasons (October–March) using the ECMWF’s ERA-Interim dataset as the verifying analysis. The Hodges cyclone-tracking scheme was used to track cyclones using 6-h mean sea level pressure (MSLP) data. For lead times less than 72 h, the NCEP and ECMWF ensembles have comparable mean absolute errors in cyclone intensity and track, while the CMC errors are larger. For days 4–6 ECMWF has 12–18 and 24–30 h more accuracy for cyclone intensity than NCEP and CMC, respectively. All ensembles underpredict relatively deep cyclones in the medium range, with one area near the Gulf Stream. CMC, NCEP, and ECMWF all have a slow along-track bias that is significant from 24 to 90 h, and they have a left-of-track bias from 120 to 144 h. ECMWF has greater probabilistic skill for intensity and track than CMC and NCEP, while the 90-member multimodel ensemble (NCEP + CMC + ECMWF) has more probabilistic skill than any single ensemble. During the medium range, the ECMWF + NCEP + CMC multimodel ensemble has the fewest cases (1.9%, 1.8%, and 1.0%) outside the envelope compared to ECMWF (5.6%, 5.2%, and 4.1%) and NCEP (13.7%, 10.6%, and 11.0%) for cyclone intensity and along- and cross-track positions.