Journal of Meteorological Research最新文献

High-quality and accurate precipitation estimations can be obtained by integrating precipitation information measures using ground-based and spaceborne radars in the same target area. Estimating the true precipitation state is a typical inverse problem for a given set of noisy radar precipitation observations. The regularization method can appropriately constrain the inverse problem to obtain a unique and stable solution. For different types of precipitation with different prior distributions, the L₁ and L₂ norms were more effective in constraining stratiform and convective precipitation, respectively. As a combination of L₁ and L₂ norms, the Huber norm is more suitable for mixed precipitation types. This study uses different regularization norms to combine precipitation data from the C-band dual-polarization ground radar (CDP) and dual-frequency precipitation radar (DPR) on the Global Precipitation Measurement (GPM) mission core satellite. Compared to single-source radar data, the fused figures contain more information and present a comprehensive precipitation structure encompassing the reflectivity and precipitation fields. In 27 precipitation cases, the fusion results utilizing the Huber norm achieved a structural similarity index measure (SSIM) and a peak signal-to-noise ratio (PSNR) of 0.8378 and 30.9322, respectively, compared with the CDP data. The fusion results showed that the Huber norm effectively amalgamate the features of convective and stratiform precipitation, with a reduction in the mean absolute error (MAE; 16.1% and 22.6%, respectively) and root-mean-square error (RMSE; 11.7% and 13.6%, respectively) compared to the 1-norm and 2-norm. Moreover, in contrast to the fusion results of scale recursive estimation (SRE), the Huber norm exhibits superior capability in capturing the localized precipitation intensity and reconstructing the detailed features of precipitation.

Tropical cyclones (TCs), including tropical depressions and different categories of typhoons, hurricanes, and cyclonic storms, mostly originate over the oceans in the absence of direct observations. Thus, detailed monitoring and analysis of TCs has always been an unsolved problem. In the recent 20 years, great changes have taken place in domestic and foreign TC monitoring techniques, imposing a significant impact on TC operations and research. Some new technologies and products gradually emerge to support operations, with improved monitoring accuracy. In this paper, the progress on TC monitoring and analysis via meteorological satellites, radars, and airplanes in China and the world is reviewed, compared, and summarized, with special focuses on multisatellite fusion observations, in situ aircraft measurements, and some unconventional observation equipment such as rockets, saildrones, and underwater gliders. On this basis, the paper points out future directions for improving TC monitoring and analysis in aid of better TC forecast and early warning.

With the launch of the first civilian early-morning orbit satellite Fengyun-3E (FY-3E), higher demands are placed on the accuracy of radiative transfer simulations for hyperspectral infrared data. Therefore, several key issues are investigated in the paper. First, the accuracy of the fast atmospheric transmittance model implemented in the Advanced Research and Modeling System (ARMS) has been evaluated with both the line-by-line radiative transfer model (LBLRTM) and the actual satellite observations. The results indicate that the biases are generally less than 0.25 K when compared to the LBLRTM, while below 1.0 K for the majority of the channels when compared to the observations. However, during both comparisons, significant biases are observed in certain channels. The accuracy of Hyperspectral Infrared Atmospheric Sounder-II (HIRAS-II) onboard FY-3E is comparable to, and even superior to that of the Cross-track Infrared Sounder (CrIS) onboard NOAA-20. Furthermore, apodization is a crucial step in the processing of hyperspectral data in that the apodization function is utilized as the instrument channel spectral response function to produce the satellite channel-averaged transmittance. To further explore the difference between the apodized and unapodized simulations, Sinc function is adopted in the fast transmittance model. It is found that the use of Sinc function can make the simulations fit the original satellite observations better. When simulating with apodized observations, the use of Sinc function exhibits larger deviations compared to the Hamming function. Moreover, a correction module is applied to minimize the impact of Non-Local Thermodynamic Equilibrium (NLTE) in the shortwave infrared band. It is verified that the implementation of the NLTE correction model leads to a significant reduction in the bias between the simulation and observation for this band.

Owing to limited observations, it remains unknown whether the impact of El Niño–Southern Oscillation (ENSO) on the Indian Ocean–Northwest Pacific (IO–NWP) climate showed decadal changes in the early 20th century. Using multi-source reanalysis and hindcast datasets from the ECMWF and NOAA extending back to 1901, this study investigates interdecadal variations of the impact of ENSO on the IO–NWP climate from 1901 to 2009. It is found that the influence of ENSO on the IO-NWP climate shows “strong–weak–strong” interdecadal change during 1901–2009. This is characterized by much weaker Indian Ocean sea surface temperature (SST) warming and a weaker NWP subtropical anticyclone (NWPSA) in the following summer of El Niño during 1946–1967, compared with those in the other two periods (1901–1945 and 1968–2009). Analyses of the datasets indicate that the interdecadal variation is mainly associated with the change in ENSO amplitude. In contrast to the period of 1946–1967, a greater SST variance occurred in the central–eastern equatorial Pacific during 1901–1945 and 1968–2009. A stronger El Niño tends to generate more significant anticyclonic anomalies over the southeast Indian Ocean through teleconnection. The northwesterly anomalies to the south of the anticyclone weaken the southeast trade winds and warm the south Indian Ocean SST via wind–evaporation–SST feedback, and the positive south Indian Ocean SST anomalies trigger westward-propagating oceanic Rossby waves to induce stronger warming of the southwest Indian Ocean, leading to a significant asymmetric wind pattern across the equator in spring. The profound northeastward winds on the north side weaken the southwest monsoon, leading to a “second warming” over the north Indian Ocean in summer, which anchors the eastward-propagating warm Kelvin waves and results in a stronger NWPSA by inducing surface divergence and suppressing deep convection.

The Northeast China cold vortex (NCCV) is one of the main synoptic-scale systems causing short-duration heavy rainfall (SDHR) in Northeast China. Environmental conditions (e.g., water vapor, instability, and vertical wind shear) are known to be distinctly different over the four quadrants of NCCVs, rendering prediction of the SDHR related to NCCVs (NCCV_SDHR) more challenging. Based on 5-yr hourly rainfall observations from 3196 automatic weather stations and ERA5 reanalysis data, 10,232 NCCV_SDHR events were identified and divided into four quadrant groups according to their relative position to the center of the NCCV (CVC). The results show that the southeast quadrant features the highest frequency of SDHR, with stronger intensity, longer duration, and wider coverage; and the SDHR in different quadrants presents different formation mechanisms and varied temporal evolution. A new coordinate system is established relative to the CVC that uses the CVC as the origin and the radius of the NCCV (r_CV) as the unit distance. In this new coordinate system, all of the NCCV_SDHR events in the 5-yr study period are synthesized. It is found that the occurrence frequency of NCCV_SDHR initially increases and then decreases with increasing distance from the CVC. The highest frequency occurs mainly between 0.8 and 2.5 times r_CV from the CVC in the southeast quadrant. This can be attributed to the favorable conditions, such as convergence of the low-level shear line and abundant water vapor, which are concentrated in this region. Furthermore, high-frequency NCCV_SDHR larger than 50 mm (NCCV_SDHR50) is observed to be closer to the CVC. When NCCV_SDHR50 occurs, the NCCV is in closer proximity to the subtropical high, resulting in stronger low-level convergence and more abundant water vapor. Additionally, there are lower lifting condensation levels and stronger 0–6- and 0–1-km vertical wind shears in these environments. These findings provide a valuable reference for more accurate prediction of NCCV_SDHR.

Relatively little is known about the impact of global warming on the tropical cyclone (TC) outflow, despite its large contribution to TC intensity. In this study, based on the International Best Track Archive for Climate Stewardship (IBTrACS) dataset and ERA5 reanalysis data, we show that the TC outflow height has risen significantly (48.20 ± 22.18 m decades⁻¹) in the past decades (1959–2021) over the western North Pacific, and the rising trend tends to be sharper for stronger TCs (the uptrend of severe typhoon is 61.09 ± 40.92 m decades⁻¹). This rising trend of the outflow height explains the contradiction between the decrease trend of the TC outflow temperature and the increase trend of the atmospheric troposphere temperature. Moreover, possible contribution of the TC outflow height uptrend to TC intensity has also been investigated. The results show that the rise of outflow height leads to the decrease of outflow temperature, and thus an increased difference between underlying sea surface temperature (SST) and TC outflow temperature, which eventually favors the increase of TC intensity.

The Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), a type of online coupled chemistry-meteorology model (CCMM), considers the interaction between air quality and meteorology to improve air quality forecasting. Meteorological data assimilation (DA) can be used to reduce uncertainty in meteorological field, which is one factor causing prediction uncertainty in the CCMM. In this study, WRF-Chem and three-dimensional variational DA were used to examine the impact of meteorological DA on air quality and meteorological forecasts over the Korean Peninsula. The nesting model domains were configured over East Asia (outer domain) and the Korean Peninsula (inner domain). Three experiments were conducted by using different DA domains to determine the optimal model domain for the meteorological DA. When the meteorological DA was performed in the outer domain or both the outer and inner domains, the root-mean-square error (RMSE), bias of the predicted particulate matter (PM) concentrations, and the RMSE of predicted meteorological variables against the observations were smaller than those in the experiment where the meteorological DA was performed only in the inner domain. This indicates that the improvement of the synoptic meteorological fields by DA in the outer domain enhanced the meteorological initial and boundary conditions for the inner domain, subsequently improving air quality and meteorological predictions. Compared to the experiment without meteorological DA, the RMSE and bias of the meteorological and PM variables were smaller in the experiments with DA. The effect of meteorological DA on the improvement of PM predictions lasted for approximately 58–66 h, depending on the case. Therefore, the uncertainty reduction in the meteorological initial condition by the meteorological DA contributed to a reduction of the forecast errors of both meteorology and air quality.

Each physical process in a numerical weather prediction (NWP) system may have many different parameterization schemes. Early studies have shown that the performance of different physical parameterization schemes varies with the weather situation to be simulated. Thus, it is necessary to select a suitable combination of physical parameterization schemes according to the variation of weather systems. However, it is rather difficult to identify an optimal combination among millions of possible parameterization scheme combinations. This study applied a simple genetic algorithm (SGA) to optimizing the combination of parameterization schemes in NWP models for typhoon forecasting. The feasibility of SGA was verified with the simulation of Typhoon Mujigae (2015) by using the Weather Research and Forecasting (WRF) model and Typhoon Higos (2020) by using the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) modeling system. The results show that SGA can efficiently obtain the optimal combination of schemes. For Typhoon Mujigae (2015), the optimal combination can be found from the 1,304,576 possible combinations by running only 488 trials. Similar results can be obtained for Typhoon Higos (2020). Compared to the default combination proposed by the COAWST model system, the optimal combination scheme significantly improves the simulation of typhoon track and intensity. This study provides a feasible way to search for the optimal combinations of physical parameterization schemes in WRF and COAWST for more accurate typhoon simulation. This can help provide references for future development of NWP models, and for analyzing the coordination and adaptability of different physical process parameterization schemes under specific weather backgrounds.

It is known that different relationships exist between the strength and displacement of the stratospheric polar vortex (SPV), and the surface air temperature (SAT) patterns in Eurasia and North America, but the mechanisms behind these relationships remain unclear, especially on an interannual timescale. Based on empirical orthogonal function (EOF) analysis using NCEP reanalysis data over 1958–2018, this study attempts to ascertain the relationship between the SPV intensity and displacement over the Arctic and the SATs in the midlatitudes of the Northern Hemisphere. Our results indicate that a strengthened SPV corresponds to an SAT increase in Eurasia and a decrease in eastern North America and Greenland. When the SPV is shifted towards Eurasia, however, a corresponding SAT increase occurs in both North America and Eurasia, with a larger increase in North America than in Eurasia. Specifically, a strengthened SPV tends to correspond to a positive North Atlantic Oscillation-like circulation in the troposphere with negative geopotential height (GH) anomalies in Greenland and eastern North American continent and positive GH anomalies to the north of 45°N in Eurasia, which corresponds to lower SATs in North America than in Eurasia. However, when the SPV shifted towards Eurasia, it was accompanied by a positive Pacific/North American-like pattern with a deepened Aleutian low, which corresponds to the increasing SATs in North America. These tropospheric circulation changes are related to the response of tropospheric planetary wave activity to the SPV. A strengthened SPV corresponds to the weakening of tropospheric planetary wave-1 waves, which is accompanied by a negative GH in North America but a positive GH in Eurasia. If the SPV shifted towards Eurasia, the tropospheric planetary wave-1 (-2) waves strengthened (weakened), and the combined effects of the planetary wave-1 and wave-2 waves would cause positive GH anomalies in both Eurasia and North America.

To further understand the prediction skill for the interannual variability of the sea ice concentration (SIC) in specific regions of the Arctic, this paper evaluates the NCEP Climate Forecast System version 2 (CFSv2), in predicting the autumn SIC and its interannual variability over the Barents–East Siberian Seas (BES). It is found that CFSv2 presents much better prediction skill for the September SIC over BES than the Arctic as a whole at 1–6-month leads, and high prediction skill for the interannual variability of the SIC over BES is displayed at 1–2-month leads after removing the linear trend. CFSv2 can reasonably reproduce the relationship between the SIC over BES in September and such factors as the surface air temperature (SAT), 200-hPa geopotential height, sea surface temperature (SST), and North Atlantic Oscillation. In addition, it is found that the prescribed SIC initial condition in August as an input to CFSv2 is also essential. Therefore, the above atmospheric and oceanic factors, as well as an accurate initial condition of SIC, all contribute to a high prediction skill for SIC over BES in September. Based on a statistical prediction method, the contributions from individual predictability sources are further identified. The high prediction skill of CFSv2 for the interannual variability of SIC over BES is largely attributable to its accurate predictions of the SAT and SST, as well as a better initial condition of SIC.