Meteorology and Atmospheric Physics最新文献

Because of the impact of the El Niño southern oscillation (ENSO) on climate and the economy, there has been extensive research on predicting its behavior. The literature on climatic forecasting falls into two broad categories, physics and time series models, the latter encompassing both statistical methods and artificial intelligence. This study compares nonlinear regressions, physics models and a combined model in which the physics forecasts are used as inputs in a neural net. The regressions are estimated in first differences, and use lags of the sea surface temperature in the equatorial Pacific. The physics forecasts are from the Seasonal-to-Multiyear Large Ensemble (SMYLE) database, which uses the Community Earth System Model version 2 (CESM2) run at the National Center for Atmospheric Research (NCAR). The physics model is tested with and without bias correction. The bias correction uses an adjustment factor calculated from earlier simulations. The combined model uses long lags of sea surface temperature and the physics forecasts. Forecasting experiments are run over 1–24-month horizons, starting at four inception points. The errors are then sorted by lead times, and ensemble averages are taken. Although the regressions capture more of the dependence between proximate values, their accuracy falls away rapidly as the horizon extends. The accuracy of the physics models is found to fluctuate substantially over the forecast horizon. Bias correction improves at some but not all horizons. The combined model achieves the most accurate forecasts at the majority of lead times, although there are cases where it is less accurate. Despite the ambiguity of the findings, the results suggest that the most promising approach is to combine physics models with artificial intelligence techniques.

Squall lines (SLs) are convective phenomena frequently occurring in the tropical atmosphere and have been widely investigated by the scientific community. In this work, satellite images of the central Amazon region were used to identify the occurrence of SLs between 2014, considered as a typical year, and 2015, characterize by a strong El Nino. Subsequently, fast response data from the Amazon Tall Tower Observatory (ATTO) site were used to establish the effects of SLs on surface parameters and the differences between the typical and strong El Niño years. The objective of this study was to investigate whether there is an influence in El Niño years on the number of occurrences of SLs and consequently on the impact that these phenomena exert on the variables, such as: precipitation, temperature, relative humidity, radiation and turbulent fluxes calculated by the eddy covariance method. Average daily cycles of these variables were used for different seasons (dry and rainy) for both years. When SLs were detected, increasing in (i) precipitation rates; (ii) wind speed; (iii) relative humidity; and (2) decreasing in (i) air temperature; (ii) shortwave radiation; (iii) sensible and latent heat flux were observed. The CO₂ flux, on the other hand, reversed its sign during the presence of SLs, in both observed years. The influence of the El Niño phenomenon in the SLs formation and their impact on the meteorological quantities (turbulent fluxes and thermodynamics variables) measured just above the canopy top is discussed.

This study presents a synoptic classification of heavy rainfall events (HRE) in the metropolitan region of Porto Alegre (MRPA) in northeastern Rio Grande do Sul (RS) State between 2000 and 2022. A total of 166 HRE were identified, characterized by daily accumulations exceeding 34 mm. The classification revealed four primary synoptic patterns associated with HRE in MRPA. Two of these patterns were linked to the movement of a typical cold front, while the other two were associated with cyclogenetic processes. In three of the patterns, two days before the occurrence of HRE in MRPA, anomalous northwesterly winds at the 850 hPa level were observed, influenced by the presence of the North-Western Argentinean Low (NAL) or Chaco Low (CHL). These winds intensified the advection of warm and moist air over a significant part of the RS State, favoring in increased rainfall in the MRPA. The four synoptic patterns showed a convective system beginning one day before the occurrence of HRE in MRPA, with the synoptic pattern associated with cyclogenesis in Uruguay exhibiting the most intense Mesoscale convective system (MCS) around the MRPA. Finally, the synoptic pattern associated with cyclogenesis in the eastern part of Santa Catarina (SC) State featured anomalous easterly winds over the eastern RS State since Day -1. This pattern facilitated increased surface mass convergence, leading to the intensification of rainfall in the MRPA.

Ensemble characteristics of a 10-member analog ensemble (AE) system for simultaneous prediction of six surface meteorological variables are examined at six station locations in the north-west Himalaya (NWH), India for lead times, 0 h (0 h)[d0], 24 h (d1), 48 h (d2) and 72 h (d3). The maximum (MMX), minimum (MNX) and mean (ME) values of each variable in analog days are found to exhibit statistically significant positive correlations with their corresponding observations at each station location for d0 through d3. The MEs of the variables are found to reproduce statistics (temporal mean, temporal standard deviation), empirical distributions of the observations on the variables reasonably well, and the MEs of the variables exhibit reasonable values of the RMSEs for d0 through d3. The observations on each variable and multiple variables simultaneously fall within their ranges (MMXs, MNXs) in ensemble members for maximum number of days for all lead times. The AE system is found to exhibit high spatial and temporal consistency in its predictive characteristics at six station locations in the NWH. Despite our short length data, these results are very interesting and suggest practical utility of the AE system for simultaneous prediction of variables at local scale utilizing local scale surface meteorological observations. Similar studies on various other types of ensemble systems can help to assess their practical utility for various forecasting applications.

Three high resolution large eddy simulations (LES) with two bulk air–sea flux algorithms, including the effects of water phase transition, are performed in order to study the influence of sea spray on the marine atmospheric boundary layer (MABL) structure and cloud properties. Because sea spray has a notable impact under severe wind conditions, the CBLAST-Hurricane experiment supplies the initial realistic conditions as well as turbulence measurements for their assessment. However a hurricane boundary layer (HBL) simulation is not in the scope of this study. Although the simulations in the final state depart from the initial conditions, all three momentum flux distributions are found at the low end of the observed range. The spray-mediated sensible heat flux is opposite to the interfacial flux and reaches up to 60% of its magnitude. When the spray-mediated contribution is taken into consideration, the simulated moisture flux increases by up to 45% and gets closer to the observations. Small scale stream-wise velocity streaks are arranged, probably due to spray effects, into large scale structures where the scalars' variations tend to concentrate. However, the vertical velocity structure below mid-MABL is not greatly affected as the buoyancy forces locally within these structures are negligible. Spray effects greatly enhance the magnitude of the quadrant components of the scalar fluxes, but the net effect is less pronounced. Spray-mediated contribution results in more extended cloud decks in the form of marine stratocumulus with increased liquid water content. The visually thicker clouds reduce the total surface radiation by up to 30 ({text{Wm}}^{-2}).

The variability of Indian summer monsoon rainfall (ISMR) has a socioeconomic impact on India. The profound relationship between ISMR and El Nino southern oscillation (ENSO) is getting weaker, due to which the impact of other climate modes has increased. Mid-latitude interaction with the monsoonal flow has increased in recent decades. Azores high, a high-pressure cell over the north Atlantic, modulates the mid-latitude wave pattern over the Eurasian region, consequently affecting Asian jet and Tibetan High. Accordingly, the repositioning of Tibetan High has shifted the ISMR band westward, causing above-normal rainfall in west and central India and below-normal rainfall in east and northeast India. The ISMR has significantly decreased over the Gangetic Plain, adversely affecting this region. This case study for the year 2022 summer monsoon has reflected one of the pieces of evidence of subdued rainfall over Gangetic Plain of India. The situation is unique because normal to above-normal rainfall was observed over the rest of the country. After analyzing various parameters, it is observed that the surface pressure anomaly over north India is against climatology, suggesting a rise in surface pressure and hence, weakening of the monsoon trough over the Gangetic Plain. This weak monsoon trough over the Gangetic Plain has reduced the monsoonal flow towards this region. Also, the strengthened Azore’s High impact through midlatitude waves reinforced the large deficit of ISMR over the Gangetic Plain during 2022.

A fast neural network technique for retrieving vertical profiles of atmospheric temperature and water vapor from the hyperspectral infrared instrument in all-sky conditions is proposed in this study. This technique inherits from the piecewise-defined neural network (PDNN) algorithm that is presently employed operationally for the FengYun-3E Vertical Atmospheric Sounding System (VASS). A major difference from the VASS sounding is the absence of microwave observation. Thus, a new cloud-impact classification method independent of microwave radiance is developed. Additionally, the numerical weather prediction (NWP) forecast temperature can be used as the input to help obtain profile information under cloud. Validation results demonstrate that this new methodology yields higher retrieval accuracy compared to the dual-regression (DR) method currently utilized in the Geostationary Interferometric Infrared Sounder/FengYun-4B (GIIRS/FY-4B) sounding system. Improvement in retrieval accuracy can be primarily attributed to three factors: (1) the cloud-impact classification process effectively mitigates the nonlinear dependence of spectral radiance on atmospheric variables; (2) the potential influence of spectral and radiometric calibration errors of GIIRS on retrieval is minimized by employing actual GIIRS observations for network training; and (3) the incorporation of prior temperature information from forecast models. This novel approach will be used to produce the operational temperature and humidity profile products from FY4B/GIIRS.

One of the key attributes of an ensemble prediction system (EPS) is the spread among the members. It plays a crucial role in conveying the uncertainty associated with the forecasted parameters. It is a quantitative measure of forecast uncertainty, provides a range of possible outcomes, and helps in the assessment of risk and decision making. Additionally, the spread can also serve as a diagnostic tool for assessing the reliability and variability among the ensemble members. If the spread is consistently narrow, it may indicate that the ensemble members are not diverse enough and the uncertainties may not be adequately captured resulting in sub-optimal decision making. In this study, the rainfall forecasts from two EPSs over India have been assessed during four monsoon seasons (2019–2022) with an aim to boost the ensemble spread by constructing a ‘Grand Ensemble’. The two high-resolution operational global EPSs of Ministry of Earth Science (MoES) in India are (i) National Centre for Medium Range Weather Forecasting (NCMRWF) EPS (NEPS) which has a 12 km grid, and 23 members and (ii) Global Ensemble Forecast System (GEFS) with a 12 km grid and 21 members. Both EPSs have been used for operational medium range forecasts out to Day-10 since 2018. The MoES Grand Ensemble (MGE) constructed by combining the two EPSs (NEPS & GEFS), features a higher spread and an improved Spread Vs Bias relationship compared to the constituent models. Further, the results indicate lowest CRPS in the MGE compared to the constituent EPSs, over the Indian land region. The improved performance of MGE is also demonstrated for moderate and heavy rainfall events using Brier Skill Score (BSS), Reliability Diagram and ROC curves.

A 41-year dataset from 1982 to 2022 analyzed climatic patterns influencing cyclone formation in the Bay of Bengal (BoB). Results showed a significant increase in sea surface temperature (SST) and a warming trend over the past four decades. Specific humidity increased while wind shear decreased. The moisture budget showed increased precipitation and evaporation rates, possibly due to more warming scenarios. Tropical Cyclones (TC) experienced significant increases in SST anomalies. These anomalies were higher during cyclonic than non-cyclonic years, except for 2015, due to El Niño conditions. Tropical Cyclone Heat Potential (TCHP) values increased in cyclonic years, while specific humidity (SH) anomalies increased 10–15 days before cyclone formation. Moist static energy (MSE) values increased across the BoB region, with TCs Amphan, Yaas, and Asani exhibiting significant positive relative vorticity (RV) anomalies. The Madden-Julian Oscillation (MJO) plays a crucial role in TC initiation and intensification, with recent TC demonstrating this. In general, the Empirical Orthogonal Function (EOF) analysis of SST, upper-level moisture, and low wind shear for May over the BoB reveals more conducive conditions for TC intensification. Furthermore, it is also found that the negative phase of the Indian Ocean Dipole (NIOD) associated pre-monsoon month of May has produced more intense TCs in recent years over BoB. The findings of this study will facilitate augmenting existing knowledge and understanding about the genesis and intensification of pre-monsoon TCs over BoB.

Precipitation observation data from different departments are highly complementary in the application, but there are some differences in observation equipment, data sampling methods, accuracy, and data transmission methods. To better apply the precipitation data of different departments in meteorological services, it is necessary to carry out a quality control method. In this study, rain gauge precipitation data, radar data, and satellite data from the China Meteorological Administration are used to perform collaborative quality control of precipitation data from the Ministry of Water Resources of the People’s Republic of China. The threshold value, spatial consistency, and temporal consistency are verified using meteorological station precipitation data, and the relationship thresholds between satellite and radar products and hourly precipitation are summarized and verified for consistency. Subsequently, collaborative quality control results are derived using a comprehensive scoring method. Testing this quality control method suggests that the method will not classify too many correct data as mistakes and the detection rate of incorrect data can be more than 0.7. Following quality control, the hourly precipitation error for hydrological station data fell dramatically, the False Alarm Rate decreased by 19%, and the anomalous maxima were successfully eliminated. Therefore, this collaborative quality control method can compensate for the deficiencies of a single quality-control source, thus allowing precipitation data not from the meteorological industry to be screened effectively.