Roles of horizontal and vertical tree canopy structure in mitigating daytime and nighttime urban heat island effects

Downslope windstorms are of major concern to those living in and around Boulder, Colorado, often striking with little warning, occasionally bringing clear-air wind gusts of 35–50 m s �1 or higher, and producing widespread damage. Historically, numerical models used for forecasting these events had lower than desired accuracy. This observation provides the motivation to study the potential for improving windstorm forecasting through the use of linear and nonlinear statistical modeling techniques with a perfect prog approach. A 10-yr mountain-windstorm dataset and a set of 18 predictors are used to train and test the models. For the linear model, a stepwise regression is applied. It is difficult to determine which predictor is the most important, although significance testing suggests that 700-hPa flow is selected often. The nonlinear techniques employed, feedforward neural networks (NN) and support vector regression (SVR), do not filter out predictors as the former uses a hidden layer to account for the nonlinearities in the data, whereas the latter fits a kernel function to the data to optimize prediction. The models are evaluated using root-mean-square error (RMSE) and median residuals. The SVR model has the lowest forecast errors, consistently, and is not prone to creating outlier forecasts. Stepwise linear regression (LR) yielded results that were accurate to within an RMSE of 8 m s �1 ; whereas an NN had errors of 7–9 m s �1 and SVR had errors of 4–6 m s �1 . For SVR, 85% of the forecasts predicted maximum wind gusts with an RMSE of less than 6 m s �1 and all forecasts predicted wind gusts with an RMSE of below 12 m s �1 . The LR method performed slightly better in most evaluations than NNs; however, SVR was the optimal technique.

Statistical modeling of downslope windstorms in Boulder, Colorado

It is often assumed that weather regimes adequately characterize atmospheric circulation variability. However, regime classifications spanning many months and with a low number of regimes may not satisfy this assumption. The first aim of this study is to test such hypothesis for the Euro-Atlantic region. The second one is to extend the assessment of sub-seasonal forecast skill in predicting the frequencies of occurrence of the regimes beyond the winter season. Two regime classifications of four regimes each were obtained from sea level pressure anomalies clustered from October to March and from April to September respectively. Their spatial patterns were compared with those representing the annual cycle. Results highlight that the two regime classifications are able to reproduce most part of the patterns of the annual cycle, except during the transition weeks between the two periods, when patterns of the annual cycle resembling Atlantic Low regime are not also observed in any of the two classifications. Forecast skill of Atlantic Low was found to be similar to that of NAO+, the regime replacing Atlantic Low in the two classifications. Thus, although clustering yearly circulation data in two periods of 6 months each introduces a few deviations from the annual cycle of the regime patterns, it does not negatively affect sub-seasonal forecast skill. Beyond the winter season and the first ten forecast days, sub-seasonal forecasts of ECMWF are still able to achieve weekly frequency correlations of r = 0.5 for some regimes and start dates, including summer ones. ECMWF forecasts beat climatological forecasts in case of long-lasting regime events, and when measured by the fair continuous ranked probability skill score, but not when measured by the Brier skill score. Thus, more efforts have to be done yet in order to achieve minimum skill necessary to develop forecast products based on weather regimes outside winter season.

/pdf/yearly-evolution-of-euro-atlantic-weather-regimes-and-of-jn7flle9ah.pdf

Yearly evolution of Euro-Atlantic weather regimes and of their sub-seasonal predictability

This special issue collects original and review articles on large-scale atmospheric circulation variability and its climate impacts [...]

Large-Scale Atmospheric Circulation Variability and Its Climate Impacts

Abstract: Given the threat of fossil fuel depletion, it is essential to proactively strive for carbon neutrality and promote clean, low-carbon and efficient energy use. This study used ERA5 reanalysis data to assess wind energy resources in the Pacific Northwest region. By analyzing key indicators such as wind power density, effective wind speed occurrence, and energy level occurrence, climate statistics and Empirical Orthogonal Function analysis (EOF) were used to examine the spatial distribution and long-term trend of offshore wind energy resources in the Northwest Pacific. The results suggest that there are abundant wind energy resources in this region, which are beneficial for the development of offshore wind energy. The rich areas are the East China Sea, Taiwan islands and reefs in the South China Sea and east of Japan, with prevailing wind power density (500 ∼ 2500 W/m2) and effective wind speed occurrence (80–90%) and energy level occurrence (60% ∼ 90%). Offshore wind energy resources in the Pacific Northwest are more abundant in fall and winter than in summer. The time coefficient of the first mode shows that the offshore wind energy in the Northwest Pacific has no obvious change trend, and the wind energy resources are relatively stable. 

Spatial distribution and long-term trend of wind energy in the Northwest Pacific Ocean

Extreme precipitation has often occurred in Southeastern China, while the possible mechanism is not clear. In order to bridge the scale gap between large-scale circulation and extreme precipitation, in this paper, the k-means clustering technique—a common method of weather-type (WT) analysis—was applied to regional 850-hPa wind fields. The reasonable determination of k values can make the later WT analyses more reliable. Thus, the Davies–Bouldin (BD) criterion index is used in the clustering process, and the optimal value of the k was determined. Then, we obtain and analyze the frequency, persistence, and progression of WTs. The rule of transitions from one WT to another may help explain some of the physical processes in winter. We found a special evolutionary chain (WT3→WT1→WT2→WT5→WT3) that can be used to explain the cold wave weather process in winter. Different WTs in the evolutionary chain correspond well to different stages of the cold wave weather process (gestation (WT3), outbreak (WT1), eastward withdrawal (WT2), and extinction (WT5)). In addition, we found that there are obvious differences in precipitation between December and February. After reassembling five kinds of WTs, two modes are formed: dry WTs and wet WTs. Our research shows that the intraseasonal variation of precipitation can be attributed to the fluctuation between the wet and dry WTs, and the different phases of teleconnection can correspond well with it. For example, the relative frequencies of wet WTs are higher in February. These WTs correspond to the positive phase of the WP and ENSO, the negative phase of the EA and EU, and the strong MJO state of the second, third, and eighth phase. Our work has well established the relationship between synoptic scale and large-scale circulation, which provides a reference for climate model simulation and future climate prediction.

https://www.mdpi.com/2073-4433/10/5/271/pdf

Winter Weather Regimes in Southeastern China and its Intraseasonal Variations

Cold wave events (CWEs) often cause major economic losses and serious casualties in the cold seasons, making CWEs among the most significant types of disastrous weather. Previous studies have mainly focused on disasters due to abrupt drops in surface temperatures, with less discussion of the strong winds associated with CWEs. Based on an intense CWE that occurred in late December 2020, we investigated the evolutionary mechanisms of the associated strong winds in terms of kinetic energy (KE) budget and evaluated the effects of this CWE on wind power production based on quantitative comparisons with the mean state. The results showed that the CWE occurred under favorable background conditions, which were characterized by a southward-moving transversal trough and a southeastward-moving shortwave trough in the middle troposphere. The surface high ridge that formed around Lake Baikal and the cold front around the southern periphery of the ridge were key factors related to the CWE evolution. The positive work carried out on the horizontal wind by the pressure gradient force that linked a lower tropospheric high-pressure ridge inland and a low-pressure trough offshore and the downward momentum transportation due to the descending motions behind the cold front dominated the enhancement and sustainment of the CWE-associated strong winds. The CWE contributed to wind power production by 1) increasing the wind power density (by an average of ∼1.05-fold) and 2) improving the availability of the wind to generate power, as it reduced the percentage of zero wind power generation by ∼6.4%, while maintaining the high-wind-velocity cut-out percentage.

Evolutionary mechanisms of the strong winds associated with an intense cold wave event and their effects on the wind power production

Linking sinuosity, a fairly recently developed metric, with high temperature extremes (HTEs) can be both useful and insightful to better understand the physical mechanisms behind HTEs. However, it is not clear whether there exists a relationship between the sinuosity changes and HTE changes in present and future climate conditions over southeastern China. In this paper, the anomalous characteristics of the atmospheric circulation are quantified by sinuosity. Three sinuosity metrics are used in this study: individual sinuosity (SIN), aggregate sinuosity (ASIN), and comprehensive sinuosity (CSIN). Furthermore, we examine the relationship between sinuosity changes and HTE changes in present and future climate conditions. ASIN is strongly correlated with surface air temperature (SAT). We find that the influence of individual sinuosity (SIN) at different latitudes on the SAT of southeastern China is different. The SIN of low (middle) latitude isohypses has significant positive (negative) correlations with the SAT of southeastern China. The SIN of high-latitude isohypses is rather limited and can therefore be ignored. The projected relationship between the sinuosity changes and HTE changes in the late 21st century suggests similar results. The change in SAT is related to the changes in climate variables over southeastern China in the future, and these changes increase with the increase in Z500 or V850 and the decrease in U500. Moreover, the frequencies of large (small) comprehensive sinuosity (CSIN) values at low (mid) latitudes will increase. At the end of the 21st century, Z500 isohypses at different latitudes will have an obvious poleward shift. Our results indicate that measuring the aggregate waviness of the midtropospheric flow (via sinuosity) can provide insight regarding HTEs, and the climate model output can be used to examine the future likelihood of increased HTE.

Sinuosity of Atmospheric Circulation over Southeastern China and Its Relationship to Surface Air Temperature and High Temperature Extremes

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Winter Weather Regimes in Southeastern China and its Intraseasonal Variations

Evolutionary mechanisms of the strong winds associated with an intense cold wave event and their effects on the wind power production

Sinuosity of Atmospheric Circulation over Southeastern China and Its Relationship to Surface Air Temperature and High Temperature Extremes