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Contrasting the X-band phased array radar (XPAR) with the conventional S-Band dual-polarization mechanical scanning radar (SMSR), the XPAR offers superior temporal and spatial resolution, enabling a more refined depiction of the internal dynamics within convective systems. While both SMSR and XPAR data are extensively used in monitoring and alerting for severe convective weather, their comparative application in numerical weather prediction through data assimilation remains a relatively unexplored area. This study harnesses the Weather Research and Forecasting Model (WRF) and its data assimilation system (WRFDA) to integrate radial velocity and reflectivity from the Guangzhou SMSR and nine XPARs across Guangdong Province. Utilizing a three-dimensional variational approach at a 1 km convective-scale grid, the assimilated data are applied to forecast a rainstorm event in the Pearl River Delta (PRD) on 6 June 2022. Through a comparative analysis of the results from assimilating SMSR and XPAR data, it was observed that the assimilation of SMSR data led to more extensive adjustments in the lower- and middle-level wind fields compared to XPAR data assimilation. This resulted in an enlarged convergence area at lower levels, prompting an overdevelopment of convective systems and an excessive concentration of internal hydrometeor particles, which in turn led to spurious precipitation forecasts. However, the sequential assimilation of both SMSR and XPAR data effectively reduced the excessive adjustments in the wind fields that were evident when only SMSR data were used. This approach diminished the generation of false echoes and enhanced the precision of quantitative precipitation forecasts. Additionally, the lower spectral width of XPAR data indicates its superior detection accuracy. Assimilating XPAR data alone yields more reasonable adjustments to the low- to middle-level wind fields, leading to the formation of small-to-medium-scale horizontal convergence lines in the lower levels of the analysis field. This enhancement significantly improves the model's forecasts of composite reflectivity and radar echoes, aligning them more closely with actual observations. Consequently, the Threat Score (TS) and Equitable Threat Score (ETS) for heavy-rain forecasts (>10 mm/h) over the next 5 h are markedly enhanced. This study underscores the necessity of incorporating XPAR data assimilation in numerical weather prediction practices and lays the groundwork for the future joint assimilation of SMSR and XPAR data. [ABSTRACT FROM AUTHOR]

Probability forecast is an objective product derived from ensemble forecasts and contains information of uncertainty, which has important reference value for operational decision-making services. In the traditional neighborhood ensemble probability method (NEP), the neighborhood radius is always a constant and does not conform to the scale spectrum involved in the practical weather events. Therefore, the Neighborhood Ensemble Probability based on Ensemble Agreement Scale (EAS_NEP) was introduced, and comprehensive evaluations were conducted for two Meiyu frontal heavy rainfall events in southern China, in order to verify the applicability of this method in such events and promote its popularization in practical operation. The ensemble forecasts obtained by combining the initial condition, lateral boundary and physical process of perturbations can better represent the practical prediction uncertainty. Based on this, the Grid-to-grid Probability (GP), NEP with different radii and EAS_NEP were compared for accuracy, forecast skill and other aspects of performance. The results showed that EAS_NEP can adjust the neighborhood radius adaptively according to the agreement between ensemble members and its radii tend to be lager for concentrated precipitation than dispersed precipitation. These dynamically adjusted radii not only avoid excessive smoothing and key information loss when the neighborhood radius is too large, but also eliminate the singular points caused by small radius and possess stepped spatial distribution with better continuity. In addition, quantitative evaluation results such as BS, FSS and ROC curves also confirm that EAS_NEP has improved forecasting skills compared to traditional methods, especially in the case of dispersed precipitation and high threshold situation. The results suggest that EAS_NEP has a good application prospect in the Meiyu frontal heavy rainfall events and can effectively improve the quality of probability forecast. [ABSTRACT FROM AUTHOR]

Tornadoes are extremely destructive natural disasters, and East China has become a high-incidence area for tornadoes in China in recent years. On 7 July 2013, an EF2-intensity tornado occurred in Gaoyou County, Jiangsu Province in eastern China, within a supercell storm near a Meiyu frontal system. To investigate the dynamical process of the tornado, a numerical simulation was performed using four one-way nested grids within the Advanced Regional Prediction System (ARPS). Data from a nearby operational S-band Doppler radar are assimilated using a 4D ensemble Kalman filter (4DEnKF) at 5 min intervals. Forecasts are run with a nested 50 m grid, capturing the tornado embedded within the supercell storm with a reasonable agreement with observations. The tornadogenesis processes within the simulation results are analyzed in detail, including the three-dimensional evolution of the tornado vortex. It is found that a cold surge within the rear flank downdraft region plays a key role in instigating tornadogenesis when the leading edge of the cold surge approaches a near-ground convergence center located underneath the main updraft, and the enhancement of the convergence center caused by the descending of the low-level mesocyclone is the direct cause of the rapid increase in tornado vorticity. Backward trajectories are calculated based on model output, and the origins of the parcels feeding the intensifying tornado vortex are identified. It is found that parcels from the mid-level of the rear flank downdraft region follow the cold surge, descending to the ground under the influence of the downdraft in the cold surge, and then entering the convergence center, merging into the core of the tornado and being lifted up. Vertical profiles of the mass and vorticity fluxes into the core of the tornado vortex are examined, and it is found that the near-ground airflow contributes significantly to the growth of the tornado vorticity, with the contribution increasing as it gets closer to the ground. [ABSTRACT FROM AUTHOR]

In order to explore the improvement of radar radial velocity data assimilation on the initial and forecast fields of typhoons, this study assimilates the quality-controlled radial velocity data in the case of Typhoon Linfa (2015) using the three-dimensional variational data assimilation system of the weather research and forecasting model (WRF-3DVAR), and then conducts several sensitivity experiments with different horizontal length scale factors. The results show that reducing the horizontal length scale factor of the background error covariance can effectively assimilate the micro- and meso-scale information from radar data and improve the forecasting effect of Linfa. Following the optimization of the horizontal length scale factor, the radial velocity data assimilation can improve the typhoon wind field structure, produce reasonable cyclonic wind field increments, and further improve the dynamic and thermal structure of the inner core area of the typhoon. Then, we can obtain a better initial field of model forecasting, and thus typhoon track and intensity forecasting are improved. [ABSTRACT FROM AUTHOR]

The application of lateral boundary perturbations (LBPs) helps to restore dispersion in convection-allowing ensemble forecasts (CAEFs). However, the applicability of LBPs remains unclear because of the differences between convection systems. Short-range (24 h) ensemble forecasts are carried out to explore this issue with a strong-forcing (SF) case and a weak-forcing (WF) case in East China. The dependence of LBPs on the forcing types of severe convection is investigated regarding the forecast error growth caused by the lateral boundary conditions (LBCs). The results show that the LBPs mainly influence the SF case rather than the WF case, especially after a 12-h forecast. The large-scale errors dominate in the SF case because the change in the synoptic-scale system affects the forecast error evolution. In contrast, the large-scale errors are mainly derived from the upscaling of the small-scale errors in the WF case, indicating that using LBPs is only insufficient in such a case. In sensitivity experiments that vary the magnitude of LBPs from 10% to 150% of its original value, CAEFs demonstrate more sensitive to LBPs in the SF case than in the WF case, indicating that the WF case has intrinsically limited predictability. Overall, LBPs are more suitable for the SF case, while additional perturbations from other sources are required for CAEFs in the WF case because of the limits of intrinsic predictability. [ABSTRACT FROM AUTHOR]

The study is based on the Weather Research and Forecasting Model (WRF) and the three-dimensional variational (3DVAR) data assimilation system. Based on two different control variables, the effects of radar radial velocity assimilation in forecasting of the tropical cyclone (TC) Kompasu were evaluated. The single observation experiment showed that DA_ψχ produces cyclonic increments, while DA_UV only produces increments in the same direction as the observation. DA_ψχ significantly enhances the wind field at 850 hPa with a large number of unphysical cyclonic increments. On the other hand, DA_UV produces reasonable cyclonic increments to enhance the TC. The assimilation of DA_UV makes the surface wind enhanced and the sea level pressure at the TC center reduced. The circular structure of the DA_ψχ wind field is not clear and neither is the large wind area concentrated. In addition, the DA_ψχ shows spurious convection at the high altitude of the vertical cross section, while the DA_UV presents enhanced large wind area at the bottom. The RMSE of the radial velocity is smaller during the circular assimilation in DA_UV. DA_ψχ does not improve the track forecast of Kompasu, while DA_UV shows a significant improvement by the track forecast. [ABSTRACT FROM AUTHOR]

Satellite data play an irreplaceable role in global observation data systems. Effective comprehensive application of satellite products will inevitably improve numerical weather prediction. FengYun-4 (FY-4) series satellites can provide not only radiance data but also retrieval data with high temporal and spatial resolutions. To evaluate the potential benefits of the combination application of FY-4 Advanced Geostationary Radiance Imager (AGRI) products on Typhoon Saola analysis and forecast, two group of experiments are set up with the Weather Research and Forecasting model (WRF). Compared with the benchmark experiment, whose sea surface temperature (SST) is from the National Centers for Environmental Prediction (NCEP) reanalysis data, the SST replacement experiments with FY-4 A/B SST products significantly improve the track and precipitation forecast, especially with the FY-4B SST product. Based on the above results, AGRI clear-sky and all-sky assimilations with FY-4B SST are implemented with a self-constructed AGRI assimilation module. The results show that the AGRI all-sky assimilation experiment can obtain better analyses and forecasts. Furthermore, it is proven that the combination application of AGRI radiance and SST products is beneficial for typhoon prediction. [ABSTRACT FROM AUTHOR]

For pulse Doppler radars, the widely used method for identifying second-trip echoes (STs) in the signal processing level yields significant misidentification in regions of high turbulence and severe wind shear. In the data processing level, although the novel algorithm for ST identification does not yield significant misidentification in specific regions, its overall identification performance is not ideal. Therefore, this paper proposes a hybrid method for the identification of STs using phase modulation (signal processing) and polarimetric technology (data processing). Through this approach, most of the STs are removed, whereas most of the first-trip echoes (FTs) remain untouched. Compared with the existing method using a signal quality index filter with an optimized threshold, the hybrid method exhibits superior performance (Heidke skill scores of 0.98 versus 0.88) on independent test datasets, especially in high-turbulence and severe-wind-shear regions, for which misidentification is significantly reduced. [ABSTRACT FROM AUTHOR]

This study investigated the regime-dependent predictability using convective-scale ensemble forecasts initialized with different initial condition perturbations in the Yangtze and Huai River basin (YHRB) of East China. The scale-dependent error growth (ensemble variability) and associated impact on precipitation forecasts (precipitation uncertainties) were quantitatively explored for 13 warm-season convective events that were categorized in terms of strong forcing and weak forcing. The forecast error growth in the strong-forcing regime shows a stepwise increase with increasing spatial scale, while the error growth shows a larger temporal variability with an afternoon peak appearing at smaller scales under weak forcing. This leads to the dissimilarity of precipitation uncertainty and shows a strong correlation between error growth and precipitation across spatial scales. The lateral boundary condition errors exert a quasi-linear increase on error growth with time at the larger scale, suggesting that the large-scale flow could govern the magnitude of error growth and associated precipitation uncertainties, especially for the strong-forcing regime. Further comparisons between scale-based initial error sensitivity experiments show evident scale interaction including upscale transfer of small-scale errors and downscale cascade of larger-scale errors. Specifically, small-scale errors are found to be more sensitive in the weak-forcing regime than those under strong forcing. Meanwhile, larger-scale initial errors are responsible for the error growth after 4 h and produce the precipitation uncertainties at the meso-β-scale. Consequently, these results can be used to explain under-dispersion issues in convective-scale ensemble forecasts and provide feedback for ensemble design over the YHRB. [ABSTRACT FROM AUTHOR]

A spatially and temporally varying Bayesian inflation algorithm was applied to the assimilation of the real radar data of a mesoscale convective system (MCS), which occurred on May 8–9, 2007 in Oklahoma and Texas (USA), using the ensemble square root filter (EnSRF) within the advanced regional prediction system (ARPS). For comparison purposes, two radar data assimilation and forecast experiments using Bayesian inflation and regular multiplicative inflation algorithms were performed. Results indicated that the analysis reflectivity structure of the MCS using the Bayesian inflation method appeared better than when using the multiplicative inflation method. The Bayesian inflation method also reduced the root mean square innovations (RMSIs) and increased the ensemble spreads for reflectivity and radial velocity. Furthermore, the reduction of RMSIs was related to the higher Bayesian inflation parameter assigned to the background. The flow‐dependent characteristic of the Bayesian inflation parameter means it varies with space and model state variables. This successful analysis helped improve subsequent forecast reflectivity and precipitation, especially for stratiform precipitation. It also promoted the development of the MCS with a strong surface cold pool and outflow in the convective regions. Quantitative reflectivity and precipitation forecast skills were also improved in the Bayesian inflation experiment. These encouraging results demonstrated the greater potential of using the Bayesian inflation algorithm for EnSRF radar data assimilation compared with multiplicative inflation. [ABSTRACT FROM AUTHOR]

A coupled hybrid ensemble square root filter and three-dimensional ensemble-variational (EnSRF-En3DVar) radar data assimilation system, developed for the Weather Research and Forecasting model, was applied to a mesoscale convective system (MCS) that occurred over southeastern China on 5 June 2009. This hybrid system uses the extended control variable method to combine the static and ensemble flow-dependent forecast error covariance. The potential of the hybrid EnSRF-En3DVar system was first explored by comparing the derived results with those obtained using 3DVar and EnSRF approaches alone. Assimilation results showed the hybrid EnSRF-En3DVar system reduces the root mean square innovations for reflectivity and radial velocity, and better represents the pattern of the MCS than either 3DVar or EnSRF. This successful analysis improved quantitative reflectivity and precipitation forecasting skills, and helped forecast the MCS more realistically with regard to location, structure and intensity. Moreover, the root mean square error of the forecast wind, temperature and water vapor mixing ratio were found reduced by the hybrid EnSRF-En3DVar system when compared with the other methods. Diagnoses of the forecast fields showed the hybrid EnSRF-En3DVar system increases low-level cooling and mid-level warming within the convective area. Furthermore, sensitivity experiments testing the ensemble covariance weighting factor effect in the hybrid EnSRF-En3DVar method suggested that a stronger ensemble covariance weighting value led to improved precipitation forecast results. • A new hybrid EnSRF-En3DVar radar data assimilation system is developed and applied to an MCS case. • The improvements of the hybrid approach are found in the forecasting of the MCS comparing with 3DVar and EnSRF. • A stronger ensemble covariance weighting value lead to an improved forecast results. [ABSTRACT FROM AUTHOR]

This study investigated the characteristics of extreme precipitation events associated with mesoscale convective systems (MCSs) in East China (the area east of 96°E) during 2016–17. Over the entire region, 204 events were first identified and classified into synoptic, tropical, MCS, small-scale-storm (SSS), and unclassified types. For 73 MCS-type events, further division and analysis were conducted according to the organizational modes. Results show that MCS-related events occurred most frequently near southern Fujian Province and from April to October with a peak in July. The area of occurrence shifted from the south in spring to the north in summer before going back to the south in autumn. The events occurred most commonly from afternoon to early evening, matured around late afternoon, and ended before dark. Among MCS subcategories, the longest average duration was seen in the multiple-MCS cases. Of the 15 selected multiple-MCS events, 11 were defined as early-maturing type with peak rainfall occurrence before the midpoint of duration while the others were late maturing. Although multiple-MCS events were accompanied by a southwest low-level jet, strong warm-air advection, and convective instability, early-maturing cases had stronger synoptic-scale ascent, moister environments, and smaller surface-based convective available potential energy (SBCAPE) and convection inhibition (SBCIN) at the most extreme rainfall-occurrence point. Compared to the MCS type within all extreme precipitation events over the United States, the percentage was lower in China. However, the events in China exhibit more pronounced seasonal cycle. [ABSTRACT FROM AUTHOR]

Abstract The scientific community mainly uses ensemble systems to represent various sources of uncertainties and to produce better forecasts. The inability to choose accurate initial conditions leads to failed forecasts due to the well-known butterfly effect; however, in modern weather models, researchers have paid more attention to inadequacies in the sense of physical parameterization schemes and other dynamic processes. In this study, the uncertainties caused by cumulus parameterization are represented by the stochastic perturbed parameterization tendency (SPPT) scheme, and the results are compared with those of classic schemes, including the multi cumulus parameterization scheme, the parameter-perturbed scheme, and the Gaussian-noise-perturbed tendency scheme. The impacts of these various schemes on the precipitation predictions of the Weather Research and Forecasting (WRF) model are compared for Southeast Asia. The results using the stochastic perturbed cumulus parameterization tendency (SPCPT) perturbation scheme are contrasted with those of the total parameterization tendency perturbation scheme. Compared to the other schemes, the multi cumulus parameterization scheme has better Brier skill scores (BSS) and greater spreads. However, the SPCPT scheme shows significant improvements in the root mean square error (RMSE) and the Gerrity skill score (GSS) for rainfall prediction. This result also implies that the noise pattern is critical to the ensemble system; thus, using a single error representation scheme may be insufficient to estimate the error in the cumulus parameterization process. Highlights • A new approach to estimate the error of cumulus parameterization is presented. • This approach is compared with six different ensemble predict methods. • The new approach can significantly improve the rainfall prediction. • One approach is insufficient to represent all error in cumulus parameterization. [ABSTRACT FROM AUTHOR]

Aerosol optical depth (AOD) is the most basic parameter that describes the optical properties of atmospheric aerosols, and it can be used to indicate aerosol content. In this study, we assimilated AOD data from the Fengyun-3A (FY-3A) and MODIS meteorological satellite using the Gridpoint Statistical Interpolation three-dimensional variational data assimilation system. Experiments were conducted for a dust storm over East Asia in April 2011. Each 0600 UTC analysis initialized a 24-hWeather Research and Forecasting with Chemistry model forecast. The results generally showed that the assimilation of satellite AOD observational data can significantly improve model aerosol mass prediction skills. The AOD distribution of the analysis field was closer to the observations of the satellite after assimilation of satellite AOD data. In addition, the analysis resulting from the experiment assimilating both FY-3A/MERSI (Medium-resolution Spectral Imager) AOD data and MODIS AOD data had closer agreement with the ground-based values than the individual assimilation of the two datasets for the dust storm over East Asia. These results suggest that the Chinese FY-3A satellite aerosol products can be effectively applied to numerical models and dust weather analysis. [ABSTRACT FROM AUTHOR]

Besides the traditional hybrid covariance data assimilation (referred to as “HCDA” in this paper) method, the hybrid gain data assimilation (referred to as “HGDA”) has been proposed recently to combine the ensemble Kalman filter and variational methods, showing potential advantages in global models. To evaluate the impact of HGDA on regional and meso-scale numerical weather prediction using WRF model over east China, both single observation tests and full cycling experiments for 3-weeks in July 2013 were conducted using the 3DVar, EnKF, HCDA and HGDA methods. The results of single observation tests showed that the analysis increments of HGDA retained more characteristics of the EnKF than HCDA because of utilizing the EnKF analysis ensemble mean in the re-center step. Both the hybrid data assimilation methods showed superiority over the pure EnKF and 3DVar in full cycling experiments. The average RMSE of HGDA was slightly smaller than the HCDA. It was also found that the HGDA method showed its advantage over HCDA at shorter leading time and yielded the highest precipitation score. For rainfall field, the HGDA had the best results in terms of intensity and coverage. Furthermore, the HGDA showed better results for supplying sufficient moisture conditions over rainfall area, such as precipitable water and water vapor flux. The uplift vertical velocity that contributed to the improvement of precipitation simulation was also strengthened. In general, both of the hybrid data assimilation methods showed better results than EnKF and 3DVar. Especially, the HGDA method showed advantage benefiting from the utilization of optimal EnKF analysis mean and 3DVar analysis which equals to the linearly combination of the gain matrix, considering the total error variance. [ABSTRACT FROM AUTHOR]

Using radar observations, the performances of the ensemble square root filter (EnSRF) and an indirect three-dimensional variational (3DVar) data assimilation method were compared for a mesoscale convective system (MCS) that occurred in the Front Range of the Rocky Mountains, Colorado (USA). The results showed that the root mean square innovations (RMSIs) of EnSRF were lower than 3DVar for radar reflectivity and radial velocity and that the spread of EnSRF was generally consistent with its RMSIs. EnSRF substantially improved the analysis of the MCS compared with an experiment without radar data assimilation, and it produced a slight but noticeable improvement over 3DVar in terms of both coverage and intensity. Forecast results initiated from the final analysis revealed that EnSRF generally produced the best prediction of the MCS, with improved quantitative reflectivity and precipitation forecast skills. EnSRF also demonstrated better performance than 3DVar in the prediction of neighborhood probability for reflectivity at thresholds of 20 and 35 dBZ, which better matched the observed radar reflectivity in terms of both shape and extension. Additionally, the humidity, temperature, and wind fields were also improved by EnSRF; the largest error reduction was found in the water vapor field near the surface and at upper levels. [ABSTRACT FROM AUTHOR]

A mesoscale convective system (MCS) over East China on June 5, 2009 was thoroughly analyzed using an Advanced Regional Prediction System (ARPS) ensemble square root filter (EnSRF) system. The analyzed reflectivity structure, location and intensity compared well with observation, and were substantially better than an experiment without radar data assimilation. The cold pool and wind speed in the convective regions were strengthened. With improved initial conditions, the impact of single‐moment (SM), double‐moment (DM) and triple‐moment (TM) microphysics parameterization (MP) schemes on ensemble forecasts of MCSs was evaluated. The use of multi‐moment (MM) MP schemes showed some improvements in neighborhood ensemble probability for reflectivity and precipitation. Quantitative reflectivity and precipitation forecast skills were also improved in MM forecasts, with those of the TM forecast the best. [ABSTRACT FROM AUTHOR]

The impacts of different microphysics and boundary schemes and terrain settings on the heavy rainfall over western Myanmar associated with the tropical cyclone (TC) ROANU (2016) are investigated using the Weather Research and Forecasting (WRF) model. The results show that the microphysics scheme of Purdue Lin (LIN) scheme produces the strongest cyclone. Six experiments with various combinations of microphysics and boundary schemes indicated that a combination of WRF Single-Moment 6-class (WSM6) scheme and Mellor-Yamada-Janjic (MYJ) best fits to the Joint Typhoon Warning Center (JTWC) data. WSM6-MYJ also performs the best for the track and intensity of rainfall and obtains the best statistics skill scores in the range of maximum rainfall intensity for 48-h. Sensitivity experiments on different terrain settings with Normal Rakhine Mountain (NRM), with Half of Rakhine Mountain (HRM), and Without Rakhine Mountain (WoRM) are designed with the use of WSM6-MYJ scheme. The track of TC ROANU moved northwestward in WoRM and HRM. Due to the presence of Rakhine Mountain, TC track moved into Myanmar and the peak rainfall occurred on the leeward side of the Mountain. In the absence of Rakhine Mountain, a shift in peak rainfall was observed in north side of the Mountain. [ABSTRACT FROM AUTHOR]

This study evaluates the performance of three-dimensional variational (3DVar) and a hybrid data assimilation system using time-lagged ensembles in a heavy rainfall event. The time-lagged ensembles are constructed by sampling from a moving time window of 3 h along a model trajectory, which is economical and easy to implement. The proposed hybrid data assimilation system introduces flow-dependent error covariance derived from time-lagged ensemble into variational cost function without significantly increasing computational cost. Single observation tests are performed to document characteristic of the hybrid system. The sensitivity of precipitation forecasts to ensemble covariance weight and localization scale is investigated. Additionally, the TLEn-Var is evaluated and compared to the ETKF(ensemble transformed Kalman filter)-based hybrid assimilation within a continuously cycling framework, through which new hybrid analyses are produced every 3 h over 10 days. The 24 h accumulated precipitation, moisture, wind are analyzed between 3DVar and the hybrid assimilation using time-lagged ensembles. Results show that model states and precipitation forecast skill are improved by the hybrid assimilation using time-lagged ensembles compared with 3DVar. Simulation of the precipitable water and structure of the wind are also improved. Cyclonic wind increments are generated near the rainfall center, leading to an improved precipitation forecast. This study indicates that the hybrid data assimilation using time-lagged ensembles seems like a viable alternative or supplement in the complex models for some weather service agencies that have limited computing resources to conduct large size of ensembles. [ABSTRACT FROM AUTHOR]

Chan-hom and Linfa were binary typhoons that occurred in the western North Pacific in 2015. In this study, the impacts of FY-3B satellite Microwave Humidity Sounder (MWHS) radiance observations on the analyses and forecasts of Linfa and Chan-hom are assessed. The regional Weather Research and Forecasting model and its data assimilation (DA) systems, using three-dimensional variational (3DVAR) and Hybrid (ensemble/3DVAR) methods are used. Assimilation of the FY-3B MWHS data using the 3DVAR method slightly improves the descriptive wind and temperature fields. Positive impacts on the specific humidity forecasts, for levels higher than 850 hPa, are also obvious. 3DVAR adjusts the typhoons' initial positions and their dynamic structures favorably, yielding better tracks, intensities, and precipitation forecasts, compared to the experiment run without MWHS data (control). With the Hybrid method, the water vapor information from the MWHS data better improve the analyses through multivariable correlations with the flow-dependent background error. The Hybrid method further improves the track, intensities, and precipitation forecasts. For Typhoon Linfa, with the coexistence of Typhoon Chan-hom, the Hybrid method provides a more descriptive background error covariance matrix, than using 3DVAR. Experiments on multiple binary typhoon cases are also provided to further validate the robustness of the results on the FY-3B satellite MWHS radiance data assimilation. [ABSTRACT FROM AUTHOR]

ABSTRACT Spatial and temporal variabilities of long-term (1961-2013) diurnal temperature range ( DTR) are examined in the Tibetan Plateau ( TP) based on the 71 observational stations. The relative regional contributions to DTR in the TP are studied among maximum temperature, minimum temperature, total cloud cover ( TCC), and atmospheric teleconnections. The regional annual mean DTR (average of the 71 stations) is 14.17 °C, with a clear maximum in winter (16.35 °C) and minimum in summer (12.62 °C). During 1961-2013, the DTR in the TP declines before the 1980s and shows mute change afterwards, with an annual rate of −0.20 °C decade−1 calculated by the Mann-Kendall method. The trend in DTR is primarily a consequence of greater warming in minimum temperature than maximum temperature. In summer, there are significant negative correlations between the TCC and DTR in the TP, suggesting that the decreases in the DTR are associated with variations of TCC in the region. The atmospheric circulation composite analysis between strongly positive and negative DTR in summer in the TP reveals that during the low DTR period the TP has more water vapour flux, stronger temperature advection, and strengthened southerly wind. This suggests that the atmospheric circulations have contributed to the trends in the DTR, but it is difficult to account for the specific contributions. Further investigations of the impact of global warming on the DTR in the TP are still required. [ABSTRACT FROM AUTHOR]

ABSTRACT On the basis of mean temperature, maximum temperature and minimum temperature from the updated China Homogenized Historical Temperature Data Sets, the recent warming in the Tibetan Plateau ( TP) during 1961-2005 and global warming hiatus period are examined. During 1961-2005, the mean temperature, maximum temperature and minimum temperature in the whole TP show a statistically increasing trend especially after the 1980s, with the annual rates of 0.27, 0.19 and 0.36 °C decade−1, respectively. The performance of 26 general circulation models ( GCMs) available in the fifth phase of the Coupled Model Intercomparison Project ( CMIP5) is evaluated in the TP by comparison with the observations during 1961-2005. Most CMIP5 GCMs can capture the decadal variations of the observed mean temperature, maximum temperature and minimum temperature, and have significant positive correlations with observations ( R > 0.5), with root mean squared error <1 °C. This suggests that CMIP5 GCMs can reproduce the recent temperature evolution in the TP, but with cold biases. However, most CMIP5 GCMs underestimate the observed warming rates, especially the CNRM-CM5, GISS-E2-H and MRI-CGCM3 models. There are significant positive correlations between the trend magnitudes and the anomaly of the mean temperature, maximum temperature and minimum temperature, with correlations of 0.85, 0.86 and 0.87, respectively. The warming from the observations and CMIP5 mean in the TP is significant during the global hiatus period, consistent with decreasing snow cover and albedo in the region. This study suggests that positive snow/ice-albedo feedback processes may account for ongoing surface warming in the TP despite the pause in global mean surface warming. [ABSTRACT FROM AUTHOR]

The impacts of assimilation of radar radial velocity (Vr) data for the application of analyses and forecasts for Hurricane Ike (2008) are investigated using hybrid ensemble transform Kalman filter–three-dimensional variational data assimilation method (Hybrid ETKF–3DVAR) in this study. Radar Vr observations are pre-processed with quality control procedures before they are assimilated using Weather Research and Forecasting and Data Assimilation model (WRFDA) with three-dimensional variational data assimilation (3DVAR) and hybrid method respectively. With the hybrid method, the ensemble mean is updated with the variational method, while the ensemble perturbations are updated by the ETKF. It seems that assimilating Radar Vr data with Hybrid ETKF–3DVAR method is able to adjust the hurricane initial position and dynamic structure significantly, yielding better track and intensity forecast in the data assimilation experiments. Positive temperature increments are found in Hybrid ETKF–3DVAR experiment, indicating a more realistic thermal structure of Hurricane Ike, while 3DVAR experiment produces much smoother and weaker increments with cold temperature increments near the hurricane vortex center at lower levels. Hybrid ETKF–3DVAR further improves the track and intensity forecast accuracy compared to 3DVAR. The ability of the hybrid method in providing flow-dependent background error covariance is the primary reason for its superior performance. [ABSTRACT FROM AUTHOR]

This work attempts to validate two modified Kalman gain algorithms by assimilating a single radar simulation data set into the Weather Research and Forecasting model using an Ensemble Square Root Filter. Emphasis is placed on the comparison of assimilation performance between the two modified algorithms against the classical Kalman gain algorithm when the measurement operator is non-linear. Three ideal storm-scale experiments, which are configured identically except for the different Kalman gain algorithms, are designed in parallel for this purpose. The results show that the first modified algorithm can result in a better simulation of a storm, as measured by the root mean square error (RMSE). The second algorithm can also, to some extent, reduce the RMSE of the simulation of some state vectors, but with little improvement of the estimation of storm intensity. Overall, our preliminary experiments indicate that the two modified Kalman gain algorithms can benefit the assimilation of complex numerical models when the measurement operators are non-linear, confirming the earlier theoretical analysis and the results of simple models. Further work is needed to evaluate the impact of the modified Kalman gain algorithms on the assimilation performance of ensemble-based methods. [ABSTRACT FROM AUTHOR]

The impact of assimilating radiances from the Advanced Microwave Sounding Unit-A (AMSU-A) on the track prediction of Typhoon Megi (2010) was studied using the Weather Research and Forecasting (WRF) model and a hybrid ensemble three-dimensional variational (En3DVAR) data assimilation (DA) system. The influences of tuning the length scale and variance scale factors related to the static background error covariance (BEC) on the track forecast of the typhoon were studied. The results show that, in typhoon radiance data assimilation, a moderate length scale factor improves the prediction of the typhoon track. The assimilation of AMSU-A radiances using 3DVAR had a slight positive impact on track forecasts, even when the static BEC was carefully tuned to optimize its performance. When the hybrid DA was employed, the track forecast was significantly improved, especially for the sharp northward turn after crossing the Philippines, with the flow-dependent ensemble covariance. The flow-dependent BEC can be estimated by the hybrid DA and was capable of adjusting the position of the typhoon systematically. The impacts of the typhoon-specific BEC derived from ensemble forecasts were revealed by comparing the analysis increments and forecasts generated by the hybrid DA and 3DVAR. Additionally, for 24 h forecasts, the hybrid DA experiment with use of the full flow-dependent background error substantially outperformed 3DVAR in terms of the horizontal winds and temperature in the lower and mid-troposphere and for moisture at all levels. [ABSTRACT FROM AUTHOR]

Precipitation is a critical component of the water balance, and hence its variability is critical for cryospheric and climate change in the Tibetan Plateau (TP). Mean annual and seasonal precipitation totals are compared between gridded observations interpolated to a high resolution (0.5° × 0.5°) and multiple reanalysis type-datasets during 1979-2001. The latter include two NCEP reanalyses (NCEP1 and NCEP2), two European Centre for Medium-Range Weather Forecasts (ECMWF) reanalyses (ERA-40 and ERA-Interim), three modern reanalyses [the twentieth century reanalysis (20century), MERRA and CFSR] and three merged analysis datasets (CMAP1, CMAP2 and GPCP). Observations show an increase in mean precipitation from the northwestern to the southeastern (SE) regions of the TP which are divided by an isohyet of 400 mm, and overall trends during the studied period are positive. Compared with observations, most of the datasets (NCEP1, NCEP2, CMAP1, CMAP2, ERA-Interim, ERA-40, GPCP, 20century, MERRA and CFSR) can both broadly capture the spatial distributions and identify temporal patterns and variabilities of mean precipitation. However, most multi-datasets overestimate precipitation especially in the SE where summer convection is dominant. There remain substantial disagreements and large discrepancies in precipitation trends due to differences in assimilation systems between datasets. Taylor diagrams are used to show the correlation coefficients, standard deviation, and root-mean-square difference of precipitation totals between interpolated observations and assimilated values on an annual and seasonal basis. Merged analysis data (CMAP1 and CMAP2) agree with observations more closely than reanalyses. Thus not all datasets are equally biased and choice of dataset is important. [ABSTRACT FROM AUTHOR]

ABSTRACT Based on intersatellite-calibrated high-resolution infrared radiation sounder (HIRS) upper tropospheric water vapour (UTWV) brightness temperatures, the width of the tropical belt is defined as the distance between the latitudes at which maximum HIRS UTWV brightness temperatures are recorded in both hemispheres. Poleward expansion of the tropical belt is evident during 1979-2013 on an annual basis, with an average global magnitude of 1.57° latitude per decade. Most rapid widening is evident in the west Pacific, in agreement with the strengthening of the Walker Circulation over time. This research suggests that the HIRS UTWV brightness temperatures are a good proxy to investigate the expansion of the tropical circulation in low latitudes. [ABSTRACT FROM AUTHOR]

This study examines the projected characteristics of mean temperature (Tmean), maximum temperature (Tmax) and minimum temperature (Tmin) in China during 2006–2100 from simulations of MPI-ESM-LR model within the Coupled Model Intercomparison Project Phase 5 (CMIP5). Periods of 2011–2040 and 2061–2090 are concentrated on the analysis under the three representative concentration pathway (RCP) scenarios: a high emission scenario (RCP8.5), a midrange mitigation emission scenario (RCP4.5), and a low emission scenario (RCP2.6). Under RCP8.5, the Tmean, Tmax and Tmin show pronounced warming with the annual rates of 0.43°C/decade, 0.42°C/decade, 0.45°C/decade during 2011–2040, and 0.72°C/decade, 0.70°C/decade, 0.76°C/decade during 2061–2090, which pronouncedly contributed by winter. Under RCP4.5, the Tmean, Tmax and Tmin display consistent increases during 2011–2040 with the trends of 0.29°C/decade, 0.29°C/decade, 0.30°C/decade on the annual basis, respectively, and the increases calm down for Tmean, Tmax and Tmin up to 0.14°C/decade during 2061–2090. Under RCP2.6, the Tmean, Tmax and Tmin demonstrate positive trends during 2011–2040 with the annual rates of 0.26°C/decade, 0.28°C/decade, 0.25°C/decade, respectively, and turn to negative afterwards. Moreover, the annual and seasonal Tmean, Tmax and Tmin are in agreement with the concentration of greenhouse gases in the atmosphere and reflect the variability of the radiative forcing trajectories in the RCP. For the spatial patterns, the northeastern China and the Tibetan Plateau are more sensitive and susceptible to climate change in future emission scenarios. It fails to capture the asymmetric trends for Tmax and Tmin projections, which do exist in the observations on the regional and global scales. This suggests that the projections have uncertainties in the models, and an understanding of causes is essential to improve the accuracies. [ABSTRACT FROM AUTHOR]

The newly launched Fengyun-3E (FY-3E) satellite in the early morning contributes to forming a complete three-orbit observation system, comprehensively improving the spatial coverage of satellite data. This study highlights the importance of applying the Microwave Humidity Sounder-II (MWHS-2) data from the FY-3E satellite by exploring the impact of jointly assimilating FY-3D and FY-3E MWHS-2 data under all-sky conditions on regional typhoon forecasting. Firstly, the impact of the different observation error statistics is studied based on FY-3D all-sky MWHS-2 radiance data assimilation for Typhoon Muifa (2022). The results show that the features of adaptive observation error statistics are rather consistent based on observational and meteorological information samples over different periods. Appropriate observation errors have the potential to improve typhoon track and intensity predictions, especially for rainfall forecasts. Consistent positive benefits of the typhoon intensity forecasts are obtained with the assimilation of the cloud-affected radiances from FY-3D MWHS-2. Moreover, joint assimilation experiments using FY-3D and FY-3E MWHS-2 radiances are conducted to compare the relative impact of each sensor for both clear-sky and all-sky. Results indicate improvements in the analyses of 500 hPa geopotential height and humidity with the introduction of cloud-affected radiance in joint assimilation experiments. The forecast performance of the joint assimilation method is significantly better than that with only FY-3E MWHS-2 observations in terms of the typhoon track, intensity, and precipitation. This superiority is particularly obvious in all-sky data assimilation experiments compared to clear-sky conditions. • Appropriate observation errors in FY-3D MWHS-2 all-sky data assimilation can enhance the typhoon predictions. • Assimilation of cloud-affected radiances from FY-3D MWHS-2 consistently improves the typhoon forecasts. • Typhoon track, intensity, and rainfall forecasts are improved with joint assimilation of FY-3D and FY-3E MWHS-2 radiances. [ABSTRACT FROM AUTHOR]

No-precipitation reflectivity observations from Doppler radar contain valuable information on areas lacking precipitation; however, they are often ignored in four-dimensional variational (4DVar) radar data assimilation (DA). This study incorporated a neighborhood-based scheme to assimilate no-precipitation observations as a mechanism to suppress spurious convection. The impact of the scheme on convective forecasting using 4DVar was evaluated by comparing the performance of experiments with and without assimilation (ExpCTL) using eight diverse storm cases that occurred over the central United States during summer 2016. Three no-precipitation assimilation experiments with different neighborhood radiuses of 10 (ExpR10), 30 (ExpR30), and 50 km (ExpR50) were conducted to examine the sensitivity of the scheme to neighborhood size. Results indicated that all the no-precipitation assimilation experiments significantly improved quantitative precipitation forecast skill with large reduction of the bias and the false alarm ratio from ExpCTL, as well as improving representation of the intensity and coverage of the precipitation. The horizontal wind, temperature, and water vapor were also improved, especially the latter. The scheme was found sensitive to neighborhood size and greater benefit was found in ExpR30 in comparison with ExpR10 and ExpR50. Analysis revealed that ExpR30 reduced low-level cooling and mid-level warming corresponding to decreased water vapor in areas of overpredicted and false precipitation, and it was more effective in conserving the total water content balance during cycled radar DA. The findings of this study could provide reference information for assimilation of no-precipitation observations into the Weather Research and Forecasting model using 4DVar, which would be valuable for severe weather prediction. • A neighborhood-based scheme was incorporated into 4DVar to assimilate no-precipitation observations from Doppler Radar. • The scheme significantly reduced spurious convection, and a moderate neighborhood size resulted in the best results. • The no-precipitation assimilation helps conserve balance of water content, which led to improved precipitation forecast. [ABSTRACT FROM AUTHOR]

The impact of assimilating radar radial velocity and reflectivity on the analyses and forecast of Hurricane IKE is investigated within the framework of the WRF (Weather Research and Forecasting) model and its three-dimensional variational (3DVar) data assimilation system, including the hydrometeor control variables. Hurricane IKE in the year 2008 was chosen as the study case. It was found that assimilating radar data is able to effectively improve the small-scale information of the hurricane vortex area in the model background. Radar data assimilation experiments yield significant cyclonic wind increments in the inner-core area of the hurricane, enhancing the intensity of the hurricane in the model background. On the other hand, by extending the traditional control variables to include the hydrometeor control variables, the assimilation of radar reflectivity can effectively adjust the water vapor and hydrometeors of the background, further improving the track and intensity forecast of the hurricane. The precipitation forecast skill is also enhanced to some extent with the radar data assimilation, especially with the extended hydrometeor control variables. [ABSTRACT FROM AUTHOR]

Reliable quantitative precipitation forecasting is essential to society. At present, quantitative precipitation forecasting based on weather radar represents an urgently needed, yet rather challenging. However, because the Z-R relation between radar and rainfall has several parameters in different areas, and because rainfall varies with seasons, traditional methods cannot capture high-resolution spatiotemporal features. Therefore, we propose an attention fusion spatiotemporal residual network (AF-SRNet) to forecast rainfall precisely for the weak continuity of convective precipitation. Specifically, the spatiotemporal residual network is designed to extract the deep spatiotemporal features of radar echo and precipitation data. Then, we combine the radar echo feature and precipitation feature as the input of the decoder through the attention fusion block; after that, the decoder forecasts the rainfall for the next two hours. We train and evaluate our approaches on the historical data from the Jiangsu Meteorological Observatory. The experimental results show that AF-SRNet can effectively utilize multiple inputs and provides more precise nowcasting of convective precipitation. [ABSTRACT FROM AUTHOR]

The Fengyun-4A (FY-4A) meteorological satellite, a next-generation geostationary meteorological satellite, was launched on December 11, 2016. For instance, the Advanced Geosynchronous Radiation Imager (AGRI) aboard FY-4A (AGRI/FY-4A) takes full-disk images at a 15-min interval in 14 spectral bands with the 0.5–4-km resolution. Here we developed data assimilation system based on the Gridpoint Statistical Interpolation (GSI) system in which the Aerosol Optical Depth (AOD) derived from FY-4A data were successfully assimilated for the first time. The capability to assimilate FY-4A Aerosol optical depth (AOD) with an hourly cycling configuration was then evaluated by a dust storm over East Asia during 12–14 May 2019. The analyses initialized Weather Research and Forecasting-Chemistry (WRF-Chem) model forecasts. The system is tested with FY-4 AOD, Himawari-8 AOD in experiments and then the results are compared to the Aerosol Robotic Network (AERONET) AOD observations, which serving as the independent observations. The results indicated that assimilating FY-4 AOD substantially showed much better agreement with observations than those from the control. Furthermore, the Bias and RMSE generally reduced about 20% with forecast range. This study indicates that the aerosol data assimilation using data from FY-4A can be used to improve the performance of forecast model. • It is the first attempt to assimilate FY-4A AOD with a rapid-update DA system. • It was investigated for the dust storm occurred over East Asia on 12–14 May 2019. • General positive impacts were achieved from assimilating high-frequency data. [ABSTRACT FROM AUTHOR]

A sensitivity-based initial condition (IC) perturbation method for convection-permitting ensemble forecasts (CPEFs) is presented and preliminarily tested. The distribution of the perturbations is based on the sensitivity patterns from the ensemble sensitivity analysis (ESA) and the fast-growing perturbations calculated by the breeding growth mode (BGM). Two convective precipitation cases are used to quantitatively and qualitatively evaluate the impacts of the perturbation schemes on the ensemble forecast skill based on the Weather Research and Forecasting (WRF) model. To generate the sensitivity-based IC perturbations, ESA is applied to the short-term convective precipitation cases to calculate the sensitivity patterns. The analysis reveals the influential factors related to the evolution of weather situations that impact the strength of the forecast precipitation. The sensitivity patterns are introduced to the IC perturbations by a sensitivity-based BGM (SeBGM) method proposed in this research, and the response of the forecast skill to different perturbation magnitudes is examined. Ensemble forecasts with sensitivity-based IC perturbations can adapt to changes in the weather regime and provide accurate simulations of the placement and strength of convective systems, leading to improvements in the precipitation forecast skill. • The sensitivity of convective precipitation to earlier environmental factors and weather situations is analyzed by ensemble-based sensitivity analysis (ESA) technique in two short-term convective precipitation cases. • A sensitivity-based initial condition (IC) perturbation method for convective-permitting ensemble forecasts (CPEFs) is proposed and evaluated. • The response of forecast skill to the perturbation magnitude is examined. [ABSTRACT FROM AUTHOR]

An improved approach to derive pseudo water vapor mass mixing ratio and in- cloud potential temperature was developed in this paper to better initialize numerical weather prediction (NWP) and build convective-scale predictions of severe weather events. The process included several steps. The first was to identify areas of deep moist convection, utilizing Vertically Integrated Liquid water (VIL) derived from a mosaicked 3D radar reflectivity field. Then, pseudo- water vapor and pseudo- in- cloud potential temperature observations were derived based on the VIL. For potential temperature, the latent heat initialization for stratiform cloud and moist adiabatic initialization for deep moist convection were used based on a cloud analysis method. The third step was to assimilate the derived pseudo- water vapor and potential temperature observations, together with radar radial velocity and reflectivity into a convective-scale NWP model during data assimilation cycles spanning several hours. Finally, 3-h forecasts were launched each hour during the data assimilation period. The effects of radar data and pseudo- observation assimilation on the prediction of rainfall associated with convective systems surrounding the Meiyu front in 2018 were explored using two real cases. Two sets of experiments, each including several experiments in each real case, were designed to compare the effects of assimilation radar and pseudo- observations on the ensuing forecasts. Relative to the control experiment without data assimilation and radar experiment, the analyses and forecasts of convections were found to be improved for the two Meiyu front cases after pseudo- water vapor and potential temperature information was assimilated. [ABSTRACT FROM AUTHOR]

Large‐scale environment fields play an important role in the accurate prediction of typhoons. However, regional predictions for typhoons often suffer from inadequate representation of large‐scale flow pattern such as those from global models due to limited domain size and observations employed in regional models, especially when multiple typhoons that interact concurrently occur in a regional domain. This study merges the large‐scale information from global model forecasts with the mesoscale information from regional model forecasts in the hybrid ensemble‐variational (EnVar) data assimilation by adding an analysis constraint in the EnVar cost function, which is defined by the departure of the regional model EnVar analysis from the global model fields and takes advantage of flow‐dependent ensemble background error covariance for the introduction of large scales using data assimilation. The EnVar assimilation impacts of the large‐scale fields on predictions of triple typhoons are assessed by conducting cycling assimilation and forecast experiments for a 13‐day‐long period in July 2015 when three typhoons concurrently occurred. Results show that the large‐scale constraint for EnVar can clearly improve the triple‐typhoons' track and intensity forecasts of the regional model. The large‐scale information introduced by the proposed method is also shown to reduce forecast errors of wind, temperature and humidity, respectively. Predictions of the rainfall caused by typhoons are also ameliorated. Besides, the analysis‐constrained regional predictions provide better model dynamic fields in terms of sea surface pressure, geopotential height, and water vapor transport, as well as developed typhoon structures. In addition, the adaptive bias correction for radiance assimilation presents a stable performance under the influence of introducing extra background large‐scale fields. The results indicate that the large‐scale analysis constraint introduced in the hybrid EnVar takes advantages of the multiscale information from the global model and the regional model respectively, thus improving the final results of the predictions of multiple typhoons. [ABSTRACT FROM AUTHOR]

This study developed a hybrid four-dimensional (4D) ensemble–variational (4DEnVar) radar data assimilation (DA) system for the Weather Research and Forecasting model. The 4DEnVar approach incorporated ensemble covariances at multiple time levels to assimilate observations distributed in the assimilation windows. By approximating the evolution of the background error using 4D ensemble covariance, use of the tangent linear and adjoint models was avoided. The impact of 4DEnVar radar DA on convective-scale analyses and forecasts was examined through comparison with 3DVar and 3DEnVar methods for the case of a squall line that occurred over southeastern China. In comparison with the other methods, 4DEnVar produced both smaller root mean square innovations for radar reflectivity and radial velocity and better analysis of the vertical structure of reflectivity. The corresponding relative humidity and vertical wind in convective regions were strengthened. Ultimately, 4DEnVar produced a substantially improved forecast, including improved quantitative precipitation and reflectivity forecast skill, and better representation of the squall line in terms of both areal coverage and intensity. In contrast, 3DEnVar improved the analysis and forecast modestly in comparison with 3DVar. Furthermore, sensitivity experiments indicated that a moderate assimilation window and a stronger ensemble weighting factor used in 4DEnVar could produce superior forecast results. The wind, temperature and water vapor were also improved by 4DEnVar, with the largest bias reduction for water vapor at low and middle levels. The improvements of 4DEnVar were further verified and shown effective using a mesocale convective system case and a local convection case. [ABSTRACT FROM AUTHOR]

Himawari-8, a next-generation geostationary meteorological satellite, is equipped with the Advanced Himawari Imager (AHI) that provides full-disk images of Earth every 10 min for 16 observation bands from visible to infrared. In this study, the capability to assimilate AHI Aerosol optical depth (AOD) has been developed within the Gridpoint Statistical Interpolation (GSI) system with an hourly cycling configuration and application to a dust storm over East Asia during 5–7 May 2017. Analyses were produced hourly, and 24 h forecasts were produced every 6 h within the Weather Research and Forecasting with Chemistry model. It was found that the mean bias and root-mean-square error (RMSE) after data assimilation is obviously reduced (by about 30%) when compared to the control experiment which didn't assimilate any observations during the dust storm period, which verified the positive effects of AOD data assimilation systems. In addition, aerosol analyses and forecasts with AOD data assimilation were substantially improved when compared to independent AOD observations from AERONET sites. Therefore, the AOD data assimilation system developed here could be used as a tool to generate better dust storm forecasts. • It is the first attempt to assimilate AHI AOD with a rapid-update DA system. • It was investigated for the dust storm occurred over East Asia on 5–7 May 2017. • General positive impacts were achieved from assimilating high-frequency data. [ABSTRACT FROM AUTHOR]

In order to further investigate the influence of ensemble generation methods on the storm-scale ensemble forecast (SSEF) system, a new ensemble sensitivity analysis-based ensemble transform with 3D rescaling (ET_3DR_ESA) method was developed. The Weather Research and Forecasting (WRF) Model was used to numerically simulate a squall line that occurred in the Jianghuai region in China on 12 July 2014. In this study, initial perturbations were generated via ET_3DR_ESA, and the ensemble forecast performance was compared to that of the dynamical downscaling (Down) method and the ensemble transform with 3D rescaling (ET_3DR) method. Results from a set of experiments indicate that ET_3DR_ESA linked to multi-scale environmental fields generates initial perturbations that can not only capture analysis uncertainties, but also match the actual synoptic conditions. Such perturbations produce faster ensemble spread growth, lower root-mean-square error, and a lower percentage of outliers, especially during the peak period of the squall line. In addition, ET_3DR_ESA can effectively reduce the energy dissipation on different scales through the analysis of the power spectrum. Moreover, the intensity and distribution forecasts of heavy rainfall from the ET_3DR_ESA ensemble forecast system were demonstrated to better match the observation. Furthermore, according to results of the relative operating characteristic (ROC) test, Brier score (BS), and equitable threat score (ETS), ET_3DR_ESA significantly improved the forecast skills for heavy rain (15–30 mm/12 h) and extreme rain (>30 mm/12 h), which are critical to the realization of accurate storm-scale system precipitation forecasts. In general, these results suggest that ET_3DR_ESA can be effectively applied to SSEF systems. [ABSTRACT FROM AUTHOR]

The impact of assimilating radiance data from the advanced satellite sensor GMI (GPM microwave imager) for typhoon analyses and forecasts was investigated using both a three-dimensional variational (3DVAR) and a hybrid ensemble-3DVAR method. The interface of assimilating the radiance for the sensor GMI was established in the Weather Research and Forecasting (WRF) model. The GMI radiance data are assimilated for Typhoon Matmo (2014), Typhoon Chan-hom (2015), Typhoon Meranti (2016), and Typhoon Mangkhut (2018) in the Pacific before their landing. The results show that after assimilating the GMI radiance data under clear sky condition with the 3DVAR method, the wind, temperature, and humidity fields are effectively adjusted, leading to improved forecast skills of the typhoon track with GMI radiance assimilation. The hybrid DA method is able to further adjust the location of the typhoon systematically. The improvement of the track forecast is even more obvious for later forecast periods. In addition, water vapor and hydrometeors are enhanced to some extent, especially with the hybrid method. [ABSTRACT FROM AUTHOR]

Error growth is investigated based on convection-allowing ensemble forecasts starting from 0000 UTC for 13 active convection events over central to eastern U.S. regions from spring 2018. The analysis domain is divided into the northwest (NW), northeast (NE), southeast (SE), and southwest (SW) quadrants (subregions). Total difference energy and its decompositions are used to measure and analyze error growth at and across scales. Special attention is paid to the dominant types of convection with respect to their forcing mechanisms in the four subregions and the associated difference in precipitation diurnal cycles. The discussions on the average behaviors of error growth in each region are supplemented by four representative cases. Results show that the meso-γ-scale error growth is directly linked to precipitation diurnal cycle while meso-α-scale error growth has a strong link to large-scale forcing. Upscale error growth is evident in all regions/cases but up-amplitude growth within its own scale plays different roles in different regions/cases. When large-scale flow is important (as in the NE region), precipitation is strongly modulated by the large-scale forcing and becomes more organized with time, and upscale transfer of forecast error is stronger. On the other hand, when local instability plays more dominant roles (as in the SE region), precipitation is overall less organized and has the weakest diurnal variations. Its associated errors at the γ and β scale can reach their peaks sooner and meso-α-scale error tends to rely more on growth of error with its own scale. Small-scale forecast errors are directly impacted by convective activities and have a short response time to convection while increasingly larger-scale errors have longer response times and delayed phase within the diurnal cycle. [ABSTRACT FROM AUTHOR]

The impact of stochastically perturbing the terminal velocities of hydrometeors on convective-scale ensemble forecasts of precipitation was examined. An idealized supercell storm case was first used to determine the terminal velocity error characteristics for a one-moment microphysics scheme in terms of the terminal velocities from a two-moment scheme. Two real cases were employed to evaluate the forecast skills resulting from perturbing the terminal velocities with real data. The results indicated that the one-moment scheme produced terminal velocities that were approximately three times higher than those of the two-moment scheme for snow and hail, which often resulted in overpredictions of hourly precipitation and areal accumulated precipitation. Therefore, stochastically perturbing the terminal velocities according to their error characteristics matched the observed hourly precipitation and areal accumulated precipitation better than the symmetrical perturbations. For the two-moment scheme, the symmetrical perturbations of the terminal velocities tended to produce lower falling speeds of precipitation hydrometeors; therefore, more light rain was produced. Compared to the unperturbed two-moment scheme, symmetrically perturbing the terminal velocities resulted in smaller precipitation errors when precipitation was overestimated but comparable or slightly larger precipitation errors when precipitation was underestimated. This work demonstrates the sensitivity of precipitation ensemble forecasts to terminal velocity perturbations and the potential benefits of adopting these perturbations; however, whether the perturbations actually result in significant improvements in precipitation forecast skill needs further study. [ABSTRACT FROM AUTHOR]

To obtain better performance of meteorological applications, it is necessary to distinguish radar echoes from meteorological and non-meteorological targets. After a comprehensive analysis of the computational efficiency and radar system characteristics, we propose a fuzzy logic method that is similar to the MetSignal algorithm; the performance of this method is improved significantly in weak-signal regions where polarimetric variables are severely affected by noise. In addition, post-processing is adjusted to prevent anomalous propagation at a far range from being misclassified as meteorological echo. Moreover, an additional fuzzy logic echo classifier is incorporated into post-processing to suppress misclassification in the melting layer. An independent test set is selected to evaluate algorithm performance, and the statistical results show an improvement in the algorithm performance, especially with respect to the classification of meteorological echoes in weak-signal regions. [ABSTRACT FROM AUTHOR]

The background error covariance is an important component in data assimilation systems, which largely dominates the error correlations between different analysis variables. This study aimed to assess the impact of the determination of the background error length and variance scale factors on the track and intensity forecast of hurricane systems for radar radial velocity (Vr) data assimilation. To test the sensitivity of the background error length scale and variance scale, multiple data assimilation analyses were performed using a 3D variational data assimilation method with a parameter‐sweeping strategy for the case of Hurricane Ike (2008) by varying the decorrelation length scale (LEN_SCALING) and the variance (VAR_SCALING) of the background error step by step. It is shown that, in radar Vr data assimilation, generally smaller variance scale and length scale factors tend to improve the prediction of the hurricane track. Setting both VAR_SCALING and LEN_SCALING equivalent to 0.4 provides a better track than other combinations. In particular, the impact of the variance scale factor on the track forecasts is larger than that of the length scale factor. There is no direct impact of tuning variance scales and length scales on the forecast intensity for the first few forecast hours. Tuning length scale factors less than 0.4 provides smaller forecast errors of minimum sea level pressure and maximum surface wind with increasing forecast lead time. [ABSTRACT FROM AUTHOR]

The impact of stochastically perturbed parameterizations on short-term tornadic supercell ensemble forecasts (EFs) was evaluated using two tornado cases that occurred in eastern China. The initial condition (IC) perturbations of EFs were generated by a three-dimensional variational data assimilation system with perturbed radar data. The parameterization perturbations of EFs were produced by a stochastic procedure that was applied to diffusion and microphysics parameterizations. This procedure perturbed tendencies from both parameterizations and intercept parameters (INTCPs) of the microphysics parameterizations. In addition to individually perturbing these quantities, a combination of perturbations of diffusion and INTCPs was also examined. A resampling method was proposed to handle perturbations that vary substantially, and a vertical localization was applied to the microphysics tendency perturbations. The results indicated that combining perturbations of diffusion and INTCPs produced the intensity and path forecasts of the low-level vortex (LLV) that better match observations for a weak tornado case; this combination also had a positive impact on the LLV intensity forecast for a much stronger tornado case. This combination outperformed the stochastic procedures that perturbed only diffusion or INTCPs, which indicated that it is better to use both error representations. The vertical localization prevented the temperature tendency perturbations of microphysics from always suppressing storms in negative perturbation (<0.0) areas. The negative INTCP and diffusion perturbations benefited the strong LLV, which is consistent with that of the idealized case. The current stochastic procedure could not address the LLV displacement error that is caused by the IC error. [ABSTRACT FROM AUTHOR]

Radar reflectivity observations contain valuable information on precipitation and have been assimilated into numerical weather prediction models for improved microphysics initialization. However, low-reflectivity (or so-called no rain) echoes have often been ignored or not effectively used in radar data assimilation schemes. In this paper, a scheme to assimilate no-rain radar observations is described within the framework of the Weather Research and Forecasting Model's three-dimensional variational data assimilation (3DVar) system, and its impact on precipitation forecasts is demonstrated. The key feature of the scheme is a neighborhood-based approach to adjusting water vapor when a grid point is deemed as no rain. The performance of the scheme is first examined using a severe convective case in the Front Range of the Colorado Rocky Mountains and then verified by running the 3DVar system in the same region, with and without the norain assimilation scheme for 68 days and 3-hourly rapid update cycles. It is shown that the no-rain data assimilation method reduces the bias and false alarm ratio of precipitation over its counterpart without that assimilation. The no-rain assimilation also improved humidity, temperature, and wind fields, with the largest error reduction in the water vapor field, both near the surface and at upper levels. It is also shown that the advantage of the scheme is in its ability to conserve total water content in cycled radar data assimilation, which cannot be achieved by assimilating only precipitation echoes. [ABSTRACT FROM AUTHOR]

The concept of stochastic parameterization provides an opportunity to represent spatiotemporal errors caused by microphysics schemes that play important roles in supercell simulations. In this study, two stochastic methods, the stochastically perturbed temperature tendency from microphysics (SPTTM) method and the stochastically perturbed intercept parameters of microphysics (SPIPM) method, are implemented within the Lin scheme, which is based on the Advanced Regional Prediction System (ARPS) model, and are tested using an idealized supercell case. The SPTTM and SPIPM methods perturb the temperature tendency and the intercept parameters (IPs), respectively. Both methods use recursive filters to generate horizontally smooth perturbations and adopt the barotropic structure for the perturbation r, which is multiplied by tendencies or parameters from this parameterization. A double-moment microphysics scheme is used for the truth run. Compared to the multiparameter method, which uses randomly perturbed prescribed parameters, stochastic methods often produce larger ensemble spreads and better forecast the intensity of updraft helicity (UH). The SPTTM method better predicts the intensity by intensifying the midlevel heating with its positive perturbation r, whereas it performs worse in the presence of negative perturbation. In contrast, the SPIPM method can increase the intensity of UH by either positive or negative perturbation, which increases the likelihood for members to predict strong UH. [ABSTRACT FROM AUTHOR]

Ground-based microwave radiometer (GMWR) data with high spatial and temporal resolution can improve the accuracy of weather forecasts when effectively assimilated into numerical weather prediction. Nowadays, the major method to assimilate these data is via indirect assimilation by assimilating the retrieved profiles, which introduces large retrieval errors and cannot easily be represented by an error covariance matrix. Direct assimilation, on the other hand, can avoid this issue. In this study, the ground-based version of the Radiative Transfer for the TIROS Operational Vertical Sounder (RTTOV-gb) was selected as the observation operator, and a direct assimilation module for GMWR radiance data was established in the Weather Research and Forecasting Model Data Assimilation (WRFDA). Then, this direct assimilation module was applied to assimilate GMWR data. The results were compared to the indirect assimilation experiment and demonstrated that direct assimilation can more effectively improve the model's initial fields in terms of temperature and humidity than indirect assimilation while avoiding the influence of retrieval errors. In addition, direct assimilation performed better in the precipitation forecast than indirect assimilation, making the main precipitation center closer to the observation. In particular, the improvement in the precipitation forecast with a threshold of 60 mm/6 h was obvious, and the corresponding TS score was significantly enhanced. [ABSTRACT FROM AUTHOR]

Precipitation nowcasting refers to the use of specific meteorological elements to predict precipitation in the next 0–2 h. Existing methods use radar echo maps and the Z–R relationship to directly predict future rainfall rates through deep learning methods, which are not physically constrained, but suffer from severe loss of predicted image details. This paper proposes a new model framework to effectively solve this problem, namely LSTMAtU-Net. It is based on the U-Net architecture, equipped with a Convolutional LSTM (ConvLSTM) unit with the vertical flow direction and depthwise-separable convolution, and we propose a new component, the Efficient Channel and Space Attention (ECSA) module. The ConvLSTM unit with the vertical flow direction memorizes temporal changes by extracting features from different levels of the convolutional layers, while the ECSA module innovatively integrates different structural information of each layer of U-Net into the channelwise attention mechanism to learn channel and spatial information, thereby enhancing attention to the details of precipitation images. The experimental results showed that the performance of the model on the test dataset was better than other examined models and improved the accuracy of medium- and high-intensity precipitation nowcasting. [ABSTRACT FROM AUTHOR]

ABSTRACT The ensemble transform Kalman filter based ensemble 3D variational ( ETKF-En3DVAR) data assimilation ( DA) system is employed to evaluate the potential value of assimilating radar radial wind ( Vr) data for the analysis and forecasting of Typhoon Saomai (2006). The DA system conducted cycling assimilation every 30 min when Saomai started to enter radar coverage. Within the DA cycles, the control analysis was updated by the ETKF-En3DVAR algorithm whereas the forecast ensemble perturbations in the hybrid scheme were updated by the ETKF algorithm. The benefits from the use of the flow-dependent ensemble covariance are explored by comparing the analysis increments, analysis and subsequent forecasts from the hybrid scheme with those from a pure 3DVAR using static background error covariance. Sensitivity to the horizontal correlation scale in the 3DVAR and the vertical covariance localization in the hybrid are also explored. The reduced horizontal correlation scale in the 3DVAR yields much more reasonable circulation analyses than the default scale. The vertical covariance localization scale specified in terms of geometric height instead of model levels allows for desirable spreading of Vr data to the surface. It seems that the assimilation with the hybrid method leads to further improved vortex intensity forecast and track forecast of the typhoon compared to those in the analyses from the global forecast system and 3DVAR. The results also indicated that the hybrid has a significant effect on the 12 h accumulated rainfall forecasts. Such improvements for analysis and forecast are probably due to the use of the flow-dependent background error covariance. [ABSTRACT FROM AUTHOR]