Atmospheric Science Letters最新文献

Many meteorological centers have operationally implemented global model-based ensemble prediction systems (GEPSs), making tropical cyclone (TC) forecasts from these systems available. The relatively low resolution of these GEPSs means that limits previous studies primarily focused on TC track forecasting. However, recent GEPS upgrades mean that TC intensity predictions from GEPSs are now also becoming of interest. This study focuses on the verification and comparison of the latest generation of GEPSs for TC intensity forecasts, particularly during the rapid intensification (RI) period over the western North Pacific (WP), eastern North Pacific (EP), and North Atlantic (NA) basins in 2021–2022. On average, the National Centers for Environmental Prediction (NCEP) GEPS performed best in predicting both TC intensity and RI across all three basins. Nevertheless, the exact timing of RI remains highly uncertain for these GEPS, indicating significant limitations in using GEPSs to forecast RI.

In the southwest Pacific, a meandering jet-stream in the upper troposphere is sometimes found at ~30° S during austral winters and is usually treated as a sub-tropical jet (STJ) due to its low latitude. For two contrasting cases, we have conducted analyses from two perspectives to identify the STJ and PFJ: first, using previously published qualitative criteria to identify jet-cores and second, investigating the jet-stream axes of STJ and PFJ identified using 2-PVU curves. The results showed that the chosen meandering jet-stream case at ~30° S was a merged, and for a time, a superposed STJ and PFJ. Downstream of the jet-streak, the PFJ split to the south and the STJ to the east. This is in significant contrast to the horizontally well-separated jet-stream case chosen in this study. Some processes likely contributing to the superposition of the STJ and PFJ were analyzed and discussed. The movement of PFJ that was closely associated with the movement of the low over the Tasman Sea and the convection in and near the tropical region may have played dominant roles.

When conducting large-eddy simulations (LESs) of plume dispersion in the atmosphere, crucial issue is to prescribe time-dependent turbulent inflow data. Therefore, several techniques for driving LESs have been proposed. For example, in the original recycling (OR) method developed by Kataoka and Mizuno (Wind and Structures, 2002, 5, 379–392), a mean wind profile is prescribed at the inlet boundary, the only fluctuating components extracted at the downstream position are recycled to the inlet boundary. Although the basic turbulence characteristics are reproduced with a short development section, it is difficult to generate target turbulent fluctuations consistent with realistic atmospheric turbulence. In this study, we proposed a dynamically controlled recycling (DCR) method that is a simple extension of the OR procedure. In this method, the magnitude of turbulent fluctuations is dynamically controlled to match with the target turbulent boundary layer (TBL) flow using a turbulence enhancement coefficient based on the ratio of the target turbulence statistics to the computed ones. When compared to the recommended data of Engineering Science Data Unit (ESDU) 85020, the turbulence characteristics generated by our proposed method were quantitatively reproduced well. Furthermore, the spanwise and vertical plume spreads were also simulated well. It is concluded that the DCR method successfully simulates plume dispersion in neutral TBL flows.

We investigate the impact of seasonal forecast biases in the Tropical Atlantic on the North Atlantic. The analysis uses a novel ensemble-based method to estimate the impact of tropical rainfall bias on forecasts of the Extratropical North Atlantic. The inter-ensemble spread of the forecast model is used to estimate the impact of the bias in Tropical Atlantic rainfall on the North Atlantic by selecting model members that happen to produce forecast anomalies that most closely resemble the tropical rainfall bias and using these as a proxy for the model error. The Tropical Atlantic rainfall bias impacts Rossby wave sources over the Subtropical Atlantic and there is a clear Rossby wave pattern originating from this area which is comparable to the mean bias in hindcasts. We argue that Tropical Atlantic rainfall errors explain a significant amount of the bias in seasonal forecasts over the Extratropical North Atlantic.

Convectively coupled equatorial waves are a significant source of atmospheric variability in the tropics. Current numerical models continue to struggle in simulating the coupled diabatic heating fields that are responsible for the development and maintenance of these waves. This study investigates how the diabatic fields associated with Mixed Rossby–Gravity waves (MRGs) are represented in four reanalysis products by using a unique observational dataset from the TRMM-KWAJEX (Tropical Rainfall Measuring Mission-Kwajalein Experiment) field campaign. These reanalyses include ERA5, Japanese 55-year Reanalysis (JRA-55), Climate Forecast System Reanalysis (CFSR), and Modern-Era Retrospective Analysis for Research and Applications (MERRA). We found that all four reanalyses captured the MRG structures in winds and temperature, and to a lesser degree in the humidity field except in the boundary layer. However, only the ERA5 and MERRA reanalyses captured the gradual rise and succession of the diabatic heating from boundary layer turbulence, shallow convection, cumulus congestus, and deep convection within the waves. ERA5 is the only product that also captured the gradual rise of the subgrid-scale vertical transport of moist static energy. All reanalysis products underestimated the diabatic heating from cumulus congestus. Results provide observational basis on what aspects of MRG can be trusted and what cannot in the reanalysis products.

Precipitation is an essential climate variable and a fundamental part of the global water cycle. Given its importance to society, precipitation is often assessed in climate monitoring activities, such as in those led by the Copernicus Climate Change Service (C3S). To undertake these activities, C3S predominantly uses ERA5 reanalysis precipitation. Research has shown that short-range forecasts for precipitation made from this reanalysis can provide valuable estimates of the actual (observed) precipitation in extratropical regions but can be less useful in the tropics. While some of these limitations will be reduced with future reanalyses because of the latest advancements, there is potentially a more immediate way to improve the precipitation estimate. This is to use the precipitation modelled in the Four-Dimensional Variational (4D-Var) data assimilation window of the reanalysis, and it is the aim of this study to evaluate this approach. Using observed 24-h precipitation accumulations at 5637 stations from 2001 to 2020, results show that smaller root-mean-square errors (RMSEs) and mean absolute errors are generally found by using the ERA5 4D-Var precipitation. For example, for all available days from 2001 to 2020, 87.5% of stations have smaller RMSEs. These improvements are driven by reduced random errors in the 4D-Var precipitation because it is better constrained by observations, which are themselves sensitive to or influence precipitation. However, there are regions (e.g., Europe) where larger biases occur, and via the decomposition of the Stable Equitable Error in Probability Space score, this is shown to be because the 4D-Var precipitation has a wetter bias on ‘dry’ days than the standard ERA5 short-range forecasts. The findings also highlight that the 4D-Var precipitation does improve the discrimination of ‘heavy’ observed events. In conclusion, an improved ERA5 precipitation estimate is largely obtainable, and these results could prove useful for C3S activities and for future reanalyses, including ERA6.

In early 2022, there were four low-temperature weather processes with rain and snow in Hunan Province, China. Two processes occurred on January 28–29 (referred to as the “0128” process) and February 6–7 (referred to as the “0206” process), and they have overlapping areas of heavy snowfall and high intensity of short-term snowfall. Multi-source observation data and the National Centers for Environmental Prediction (NCEP) reanalysis data are used to analyze the characteristics of circulation background and mesoscale. In addition, the causes of heavy snowfall processes under the influence of the southern branch trough are discussed based on the dual-polarization radar products at Changsha station. The results show that two processes are characterized by the rapid phase transformation of rain and snow, concentrated snowfall periods, and heavy snowfall at night. The short-term snowfall intensity of the “0206” process is greater than that of the “0128” process. The high-latitude blocking high of the “0206” process is stronger than that of the “0128” process, and the water vapor transport of the southerly jet in low levels in the “0206” process is also stronger. The organized development of cold cloud clusters from the meso-β scale to the meso-α scale indicates that the snowfall intensifies, and the maximum blackbody temperature gradient corresponds well to the center of heavy snowfall. The propagation that is similar to the train effect is an important reason for the heavy snowfall process. The vertical variation of the Z_H and the bright band of dual-polarization parameters can determine the phase transformation between rain and snow. When the Z_H and Z_DR bright bands are 1–3 km away from the ground, the phase state is rain if the Z_H near the ground is greater than 0 dBZ and the CC is close to 1; the phase state is the rain-snow mixed phase if the CC is less than 0.95. When the bottom of the Z_H bright band decreases, the CC/Z_DR bright band disappears, the near-surface CC is greater than 0.99 and the Z_DR is less than 1 dB, the rain turns to snow. Compared with the “0128” process, the characteristics of the bright ring during the rainfall period of the “0206” process are more obvious, the precipitation intensity judged from the larger Z_H and K_DP is larger, and the phase transformation is faster due to more significant cooling effect caused by precipitation.

Convolutional neural networks (CNNs) have become one of the state-of-the-art techniques for downscaling climate projections. They are being applied under Perfect-Prognosis (trained in a historical period with observations) and hybrid approaches (as Regional Climate Models (RCMs) emulators), with satisfactory results. Nevertheless, two important aspects have not been, to our knowledge, properly assessed yet: (1) their performance as emulators for other Earth System Models (ESMs) different to the one used for training, and (2) their performance under extrapolation, that is, when applied outside of their calibration range. In this study, we use UNET, a popular CNN, to assess these two aspects through two pseudo-reality experiments, and we compare it with simpler emulators: an interpolation and a linear regression. The RCA4 regional model, with 0.11° resolution over a complex domain centered in the Pyrenees, and driven by the CNRM-CM5 global model is used to train the emulators. Two frameworks are followed for the training: predictors are taken (1) from the upscaled RCM and (2) from the ESM. In both frameworks, the performance of the UNET when applied for other ESMs different to the one used for training is considerably worse, indicating poor generalization. For the linear method a similar deterioration is seen, so this limitation does not seem method specific but inherent to the task. For the second experiment, the emulators are trained in present and evaluated in future, under extrapolation. While averaged aspects such as the mean values are well simulated in future, significant biases (up to 5°C) appear when assessing warm extremes. These biases are larger by UNET than those produced by the linear method. This limitation suggests that, for variables such as temperature, with a marked signal of change and a strong linear relationship with predictors, simple linear methods might be more appropriate than the sophisticated deep learning techniques.

Using a large ensemble of initialised retrospective forecasts (hindcasts) from a seasonal prediction system, we explore various statistics relating to sudden stratospheric warmings (SSWs). Observations show that SSWs occur at a similar frequency during both El Niño and La Niña northern hemisphere winters. This is contrary to expectation, as the stronger stratospheric polar vortex associated with La Niña years might be expected to result in fewer of these extreme breakdowns. Here we show that this similar frequency may have occurred by chance due to the limited sample of years in the observational record. We also show that in these hindcasts, winters with two SSWs, a rare event in the observational record, on average have an increased surface impact. Multiple SSW events occur at a lower rate than expected if events were independent but somewhat surprisingly, our analysis also indicates a risk, albeit small, of winters with three or more SSWs, as yet an unseen event.

The increasing rate of tropical cyclone (TC) rainfall has put populations in the Western North Pacific Region at greater risk of TC rainfall-induced disasters. Statistical methodologies have shown potential in complementing existing prediction approaches. With TC track prediction accuracy significantly improving, statistical predictions have turned to TC tracks as a measure of similarity between TCs. Several studies have utilized Fuzzy C Means (FCM) to this end. However, FCM alone does not provide guidance on how many similar TCs should be used for predicting rainfall through ensemble averaging. While various number of ensemble members have been used to check the average error, such an approach yields only one number, which may not always be the most appropriate. In this study, we proposed a spatial and attribute filter to complement FCM identification of similar TCs. This filter excludes similar TCs with central pressure differences greater than 5% at strategic TC locations near land. The use of the filter yielded better rainfall prediction values than using FCM alone, as demonstrated in this study and validated against previous research findings. Our proposed model offers a reliable means of predicting TC rainfall when used in conjunction with accurately predicted TC tracks, representing a valuable complementary approach to existing prediction methods.