Meteorology and Atmospheric Physics最新文献

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.

Numerical weather models often face significant challenges in achieving high prediction accuracy. To enhance the predictive performance of these models, a solution involving the integration of deep learning algorithms has been proposed. This paper introduces a machine learning approach for correcting the numerical weather forecast results from the Weather Research and Forecasting (WRF) model. Initially, the WRF model is used to simulate summer precipitation in the Jinsha River Basin. Subsequently, the adaptive noise-robust empirical mode decomposition (CEEMDAN) method is employed to decompose WRF simulation errors. These decomposed subsequences are then input into four machine learning algorithms and two metaheuristic optimization algorithms to predict the error sequences. Finally, the predicted error subsequences are merged and superimposed on the WRF simulation values to obtain the corrected precipitation. Research findings demonstrate that the integration of machine learning algorithms with WRF significantly improves prediction accuracy. The correlation coefficient of the optimal model increases by 158%, and Nash-Sutcliffe Efficiency (NSE) increases by 149% compared to before correction. This indicates that correcting the WRF model through deep learning methods effectively enhances precipitation forecasting accuracy.

The study evaluated the impact of climate change on incoming solar radiation (RSDS) in West Africa by comparing observed data from the CMSAF solar products (SARAH and CLARA-A1) for the period 1983–2019 with simulated data from the AFR-CORDEX models (RegCM-4.7 and CCCma-canRCM4) for the historical period (1983–2004) and various RCP emission scenarios (2.6, 4.5, 8.5) for 2005–2099. The values of the RCP in parentheses signify the level of increasing radiative forcings due to varying emission controls. Assessment metrics like correlation coefficient (R), Taylor Skill Score (TSS), and root mean square errors (RMSE) were employed for comparative analysis on annual and seasonal timescales. The analyses revealed annual mean RSDS intensities of 256.22 for SARAH, 238.53 for CLARA-A1, 270.81 for Historical, 270.26 for RCP 2.6, 255.90 for RCP 4.5, and 271.93 for the RCP 8.5 scenarios in watts per square metres. The TSS analyses showed average agreement values between observed CMSAF and simulated AFR-CORDEX solar radiation with values of 0.8450 and 0.8575 with historical, 0.8750 and 0.8600 with RCP 2.6, 0.9025 and 0.8550 with RCP 4.5, and 0.8675 and 0.8525 with RCP 8.5 scenarios for SARAH and CLARA-A1 respectively. All the metrics showed better agreement with SARAH than CLARA-A1, likely due to the associated cloud influence on CLARA-A1. Notably, the CORDEX-CCCma-canRCM4 model under RCP 4.5 demonstrated the highest accuracy, with an average correlation of 0.82 and a mean TSS of 0.90 against the SARAH reference dataset. The results suggest the AFR-CORDEX model, particularly the CCCma-canRCM4 for RCP 4.5 scenario, could reliably predict solar radiation and inform climate change impacts on solar energy potential in West Africa under moderate emission conditions.

The analysis of urban thermal environment based on Local Climate Zone (LCZ) is helpful to understand the fine structure of urban heat island (UHI), so as to provide a scientific basis for urban ecological environment management. This research focused on the three biggest cities, Shanghai, Nanjing and Hangzhou, in Yangtze River Delta (YRD) and the UHI characteristics in a heatwave month (July 2017) were investigated. Based on the observations of automatic weather stations, the spatiotemporal characteristics of air temperature and canopy urban heat island intensity (UHII) of each LCZ in three cities under different weather conditions were compared and analyzed by using the LCZ clustering method, and the effects of water bodies, urban greening and sea breeze on urban heat island were discussed. Results show that the air temperature and urban heat island intensity of different LCZs would vary due to the differences in urban geometry, building materials, the proportion of impervious surface and anthropogenic heat. The LCZ based UHII in the three YRD typical cities showed similar characteristics: compact high-rise (LCZ 1), compact mid-rise (LCZ 2) and open mid-rise (LCZ 5) had higher UHII while sparsely built (LCZ 9) had lower UHII. The diurnal variation of UHII in the three cities are different: the UHII diurnal curves of Nanjing and Hangzhou were “U” type, while that of Shanghai was shallow “W” type, which was because Shanghai was vulnerable to sea breeze during the summer day. In addition to land and sea location, large water bodies and urban greening would also impact the spatiotemporal patterns of urban thermal environment.