Potential of AOD Retrieval Using Atmospheric Emitted Radiance Interferometer (AERI)

Aerosols in the atmosphere play an essential role in the radiative transfer process due to their scattering, absorption, and emission. Moreover, they interrupt the retrieval of atmospheric properties from ground-based and satellite remote sensing. Thus, accurate aerosol information needs to be obtained. Herein, we developed an optimal-estimation-based aerosol optical depth (AOD) retrieval algorithm using the hyperspectral infrared downwelling emitted radiance of the Atmospheric Emitted Radiance Interferometer (AERI). The proposed algorithm is based on the phenomena that the thermal infrared radiance measured by a ground-based remote sensor is sensitive to the thermodynamic profile and degree of the turbid aerosol in the atmosphere. To assess the performance of algorithm, AERI observations, measured throughout the day on 21 October 2010 at Anmyeon, South Korea, were used. The derived thermodynamic profiles and AODs were compared with those of the European center for a reanalysis of medium-range weather forecasts version 5 and global atmosphere watch precision-filter radiometer (GAW-PFR), respectively. The radiances simulated with aerosol information were more suitable for the AERI-observed radiance than those without aerosol (i.e., clear sky). The temporal variation trend of the retrieved AOD matched that of GAW-PFR well, although small discrepancies were present at high aerosol concentrations. This provides a potential possibility for the retrieval of nighttime AOD.

The representation of winds in the lower troposphere in ECMWF forecasts and reanalyses during the EUREC4A field campaign

The characterization of systematic forecast errors in lower-tropospheric winds over the ocean is a primary need for reforming models. Winds are among the drivers of convection, thus an accurate representation of winds is essential for better convective parameterizations. We focus on the temporal variability and vertical distribution of lower-tropospheric wind biases in operational medium-range weather forecasts and ERA5 reanalyses produced with the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). Thanks to several sensitivity experiments and an unprecedented wealth of measurements from the 2020 EUREC4A field campaign, we show that the wind bias varies greatly from day to day, resulting in RSME's up to 2.5 m s−1, with a mean wind speed bias up to −1 m s−1 near and above the trade-inversion in the forecasts and up to −0.5 m s−1 in reanalyses. The modeled zonal and meridional wind exhibit a too strong diurnal cycle, leading to a weak wind speed bias everywhere up to 5 km during daytime, turning into a too strong wind speed bias below 2 km at nighttime. The biases are fairly insensitive to the assimilation of sondes and likely related to remote convection and large scale pressure gradients. Convective momentum transport acts to distribute biases throughout the lowest 1.5 km, whereas at higher levels, other unresolved or dynamical tendencies play a role in setting the bias. Below 1 km, modelled friction due to unresolved physical processes appears too strong, but is (partially) compensated by dynamical tendencies, making this a challenging coupled problem.

Climatological Features of Squall Line at the Borneo Coastline during Southwest Monsoon

Borneo Squall Line (BSL) is a disaster risk associated with intense rain and wind gust that affect the activities and residence near the northern coast of Borneo. Using 3-hourly rainfall from Tropical Rainfall Measuring Mission (TRMM) 3B42V7 during southwest monsoon season (May–September) from 1998–2018, a total of 629 squall days were identified. Their monthly and annual average was 6 and 30 days, respectively, with July representing the month with the highest number of squall line days. BSL is frequently initiated during midnight/predawn and terminated in the morning. Composite analyses of BSL days using the daily winds from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim revealed that lower tropospheric wind convergence is a crucial controlling factor for BSL formation. The position of the monsoon trough closer to the equatorial South China Sea (SCS), and strong westerly and south-westerly winds played an important role in creating this wind convergence region. Analyses of tropical cyclone (TC) data from the Regional Specialized Meteorological Centre (RSMC), Tokyo showed that nearly 72% of BSL occurred with the presence of TC. Spectral analysis exhibited prominent frequencies mainly in the 3–4- and 6-year time scale, which likely reflected the influence of interannual modulation of El-Niño Southern Oscillation (ENSO). Correlation coefficient between squall days and Sea Surface Temperature (SST) anomalies indicated that BSL increased after La-Niña events. This study is expected to have implications for real-time squall line forecasting in Malaysia and contributes toward a better understanding of BSL.

Differences in the Sub-seasonal Predictability of Extreme Stratospheric Events

Extreme stratospheric events such as sudden stratospheric warming and strong vortex events associated with an anomalously weak or strong polar vortex can have downward impacts on surface weather that can last for several weeks to months. Hence, successful predictions of these stratospheric events would be beneficial for extended range weather prediction. However, the predictability limit of extreme stratospheric events is most often limited to around 2 weeks or less. The predictability also strongly differs between events, and between event types. The reasons for the observed differences in the predictability, however, are not resolved. To better understand the predictability differences between events, we expand the definitions of extreme stratospheric events to wind deceleration and acceleration events, and conduct a systematic comparison of predictability between event types in the European Centre for Medium-Range Weather Forecasts (ECMWF) prediction system for the sub-seasonal predictions. We find that wind deceleration and acceleration events follow the same predictability behaviour, that is, events of stronger magnitude are less predictable in a close to linear relationship, to the same extent for both types of events. There are however deviations from this linear behaviour for very extreme events. The difficulties of the prediction system in predicting extremely strong anomalies can be traced to a poor predictability of extreme wave activity pulses in the lower stratosphere, which impacts the prediction of deceleration events, and interestingly, also acceleration events. Improvements in the understanding of the wave amplification that is associated with extremely strong wave activity pulses and accurately representing these processes in the model is expected to enhance the predictability of stratospheric extreme events and, by extension, their impacts on surface weather and climate.

Wave Climate and Trends for the Marine Experimental Station of Capo Tirone Based on a 70-Year-Long Hindcast Dataset

Nearshore marine systems provide multiple economic and ecological services to human communities. Several studies addressing the climate change stressors and the inappropriate use of the sea indicate a decline of coastal areas. An extensive monitoring of the most important marine sites and protected areas is crucial to design effective environmental-friendly measures to support the sustainable development of coastal regions. A 70-year-long wave climate analysis is presented to study the climatology of the area belonging to the Marine Experimental Station of Capo Tirone, Italy. The analysis is based on the global atmospheric reanalysis developed by the European Centre for Medium-Range Weather Forecasts, validated through an observed buoy dataset recorded by the Italian Sea Wave Measurement Network. No significant long-term trends have been detected. The need to set up new monitoring stations has been pointed out by means of a hydrodynamic model developed at the regional scale, evaluating the effect of the local morphology on the nearshore wave climate and highlighting the importance of surveying the marine protected area of Capo Tirone located therein.

Characteristics of the Wind and Wave Climate along the European Seas Focusing on the Main Maritime Routes

European seas have a strong economic role both in terms of transport and tourism. Providing more knowledge, regarding the mean and extreme values of the wind and sea state conditions in the areas characterized by high maritime traffic, helps to improve navigational safety. From this perspective, six zones with high maritime traffic are studied. ERA5 database, a state-of-the-art global reanalysis dataset provided by ECMWF (European Centre for Medium-Range Weather Forecasts), is used to assess the average values and the percentiles for the wind speed and the main wave parameters in the target areas considering the period 2001–2020. The main European routes and the extreme conditions along them as well as the areas characterized by high values of wind speed and high waves were also identified. A more comprehensive picture of the expected dynamics of the environmental matrix along the most significant shipping routes is useful because in this way the most dangerous areas could be avoided by ships for the safety of passengers and transported goods.

Stochastic Modeling of Stratospheric Temperature

This study suggests a stochastic model for time series of daily zonal (circumpolar) mean stratospheric temperature at a given pressure level. It can be seen as an extension of previous studies which have developed stochastic models for surface temperatures. The proposed model is a combination of a deterministic seasonality function and a Lévy-driven multidimensional Ornstein–Uhlenbeck process, which is a mean-reverting stochastic process. More specifically, the deseasonalized temperature model is an order 4 continuous-time autoregressive model, meaning that the stratospheric temperature is modeled to be directly dependent on the temperature over four preceding days, while the model’s longer-range memory stems from its recursive nature. This study is based on temperature data from the European Centre for Medium-Range Weather Forecasts ERA-Interim reanalysis model product. The residuals of the autoregressive model are well represented by normal inverse Gaussian-distributed random variables scaled with a time-dependent volatility function. A monthly variability in speed of mean reversion of stratospheric temperature is found, hence suggesting a generalization of the fourth-order continuous-time autoregressive model. A stochastic stratospheric temperature model, as proposed in this paper, can be used in geophysical analyses to improve the understanding of stratospheric dynamics. In particular, such characterizations of stratospheric temperature may be a step towards greater insight in modeling and prediction of large-scale middle atmospheric events, such as sudden stratospheric warming. Through stratosphere–troposphere coupling, the stratosphere is hence a source of extended tropospheric predictability at weekly to monthly timescales, which is of great importance in several societal and industry sectors.